Text display method, device, electronic device and computer-readable medium

Abstract

The embodiments of the present disclosure disclose a text display method, device, electronic device and computer-readable medium. A specific implementation of the method includes: obtaining an input topic and a material file; performing semantic splitting on the input topic and the material file to obtain first semantic splitting information and second semantic splitting information; summarizing the first semantic splitting information and the second semantic splitting information to generate semantic splitting summary information; determining a topic index content set; generating topic popularity information; obtaining a topic review standard file; generating a topic circulation effect text; performing text content verification on the topic circulation effect text to generate a verification result; and performing multimodal display on the topic circulation effect text. This implementation can be applied to different topic circulation platforms, and while fully utilizing current computing resources, accurately and efficiently generates a topic circulation text corresponding to the input topic, so as to effectively display the circulation effect corresponding to the input topic in a multimodal manner.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and more particularly to a text presentation method, apparatus, electronic device, and computer-readable medium. Background Art

[0002] With the continuous development of society, discussions on various topics are becoming increasingly widespread. Predicting the spread of generated topics has become a key development direction in various fields. A common approach to predicting the spread of topics is to train a targeted prediction model (e.g., a sentiment analysis model with a predetermined network structure) for the target topic spread platform.

[0003] However, when using the above method to predict the topic flow effect, the following technical problems often occur:

[0004] In cross-platform and cross-cultural scenarios, using fixed algorithm models can be difficult to adapt to topic flow predictions, resulting in inaccurate predictions. Furthermore, using neural networks to predict topic flow often leads to irrational scheduling of computing resources, resulting in significant delays and lags in generating topic flow results.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the Invention

[0006] The content of this disclosure is used to briefly introduce concepts that will be described in detail in the detailed description section below. The content of this disclosure is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] Some embodiments of the present disclosure provide a text presentation method, apparatus, electronic device, and computer-readable medium to address one or more of the technical issues mentioned in the background technology section above.

[0008] In a first aspect, some embodiments of the present disclosure provide a text display method, comprising: obtaining an input topic input by a target user on a target input page and a material file corresponding to the input topic, wherein the material file comprises: a material file in image form, a material file in video form, and a material file in text form; performing semantic splitting on the input topic and the material file to obtain first semantic splitting information and second semantic splitting information; aggregating the first semantic splitting information and the second semantic splitting information to generate semantic splitting summary information; according to the semantic splitting summary information, utilizing the available computing resources corresponding to the central processing unit and / or the available computing resources corresponding to the image processor to determine the topic indicator content under a predetermined topic parameter indicator set, and obtain a topic indicator content set, wherein the topic indicator content set It is a content set for each topic circulation platform; based on the above topic indicator content set, the topic popularity information for the above input topic is generated; a topic review standard file is obtained, wherein the above topic review standard file includes: at least one standard information corresponding to at least one topic review direction; based on the above topic popularity information and the above topic review standard file, a pre-trained large language model is used to generate a topic circulation effect text in a target format corresponding to the above input topic, wherein the model type corresponding to the above large language model is the model type selected by the above target user in the topic circulation effect text generation page; the text content of the above topic circulation effect text is verified to generate a verification result; in response to determining that the above verification result represents that the text content in the topic text is correct, the above topic circulation effect text is multimodally displayed on the target terminal.

[0009] In a second aspect, some embodiments of the present disclosure provide a text display device, including: a first acquisition unit, configured to acquire an input topic input by a target user on a target input page and a material file corresponding to the input topic, wherein the material file includes: a material file in image form, a material file in video form, and a material file in text form; a semantic splitting unit, configured to perform semantic splitting on the input topic and the material file to obtain first semantic splitting information and second semantic splitting information; an information aggregation unit, configured to aggregate the first semantic splitting information and the second semantic splitting information to generate semantic splitting aggregation information; a determination unit, configured to determine the topic indicator content under a predetermined topic parameter indicator set based on the semantic splitting aggregation information and the available computing resources corresponding to the central processing unit and / or the available computing resources corresponding to the image processor, to obtain a topic indicator content set, wherein the topic indicator content set is for each A content set of a topic circulation platform; a first generation unit, configured to generate topic popularity information for the above-mentioned input topic based on the above-mentioned topic indicator content set; a second acquisition unit, configured to obtain a topic review standard file, wherein the above-mentioned topic review standard file includes: at least one standard information corresponding to at least one topic review direction; a second generation unit, configured to generate a topic circulation effect text in a target format corresponding to the above-mentioned input topic using a pre-trained large language model based on the above-mentioned topic popularity information and the above-mentioned topic review standard file, wherein the model type corresponding to the above-mentioned large language model is the model type selected by the above-mentioned target user in the topic circulation effect text generation page; a verification unit, configured to perform text content verification on the above-mentioned topic circulation effect text to generate a verification result; a display unit, configured to perform multimodal display of the above-mentioned topic circulation effect text on the target terminal in response to determining that the above-mentioned verification result indicates that the text content in the topic text is correct.

[0010] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

[0011] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner in the first aspect is implemented.

[0012] The above-described embodiments of the present disclosure have the following beneficial effects: The text display methods of some embodiments of the present disclosure are applicable to different topic flow platforms, accurately and efficiently generating topic flow text corresponding to an input topic while fully utilizing current computing resources, effectively displaying the corresponding input topic flow effect in a multimodal manner. Specifically, the reasons for the inaccurate flow effect of the input topic and the irrational allocation of computing resources are: the use of fixed algorithm models in cross-platform and cross-cultural scenarios has the problem of poor adaptability for predicting topic flow effects, resulting in inaccurate prediction of topic flow effects. In addition, the use of neural networks to predict topic flow effects often suffers from irrational scheduling of computing resources, resulting in significant delays and lags in the generation of topic flow effects. Based on this, the text display methods of some embodiments of the present disclosure first obtain the input topic entered by the target user on the target input page and the corresponding material file of the input topic, so as to facilitate the subsequent prediction and display of the topic flow effect for the input topic. The material files include: image-formatted material files, video-formatted material files, and text-formatted material files. In addition, by inputting on the target input page, it is possible to facilitate the uploading of topic-related information of the target user. Then, the above-mentioned input topic and the above-mentioned material file are semantically split to obtain first semantic splitting information and second semantic splitting information, so as to facilitate semantic extraction of the input topic and material file at a semantic granularity. At the same time, it is also convenient for the subsequent determination of the indicator content set corresponding to the topic parameter indicator set. Secondly, based on the above-mentioned semantic splitting summary information, the available computing resources corresponding to the central processing unit and / or the available computing resources corresponding to the image processor can be used to accurately determine the topic indicator content under the predetermined topic parameter indicator set to obtain the topic indicator content set. Among them, the above-mentioned topic indicator content set is a content set for each topic circulation platform. The obtained topic indicator content set is used for the subsequent generation of topic popularity information. Moreover, by considering the amount of available computing resources corresponding to the central processing unit and the amount of available computing resources corresponding to the image processor, the corresponding computing resources can be prepared in advance for the generation of the topic indicator content set, avoiding the problem of large delays and freezes in the generation of topic flow effects.

[0013] Next, based on the aforementioned topic indicator content set, topic popularity information for the input topic can be accurately generated. Accurately generating this topic popularity information can characterize the corresponding circulation heat of the input topic. Next, a topic review standard file is obtained to facilitate subsequent evaluation of topic circulation for at least one topic review direction, thereby generating a more accurate topic circulation effect text. The aforementioned topic review standard file includes at least one standard information corresponding to at least one topic review direction. Furthermore, based on the aforementioned topic popularity information and the aforementioned topic review standard file, a pre-trained large language model can be utilized to accurately generate a topic circulation effect text in a target format corresponding to the input topic. The large language model corresponds to the model type selected by the target user on the topic circulation effect text generation page. The generated topic circulation effect text can fully demonstrate the circulation effect of the input topic under each topic review direction, making the circulation effect of the input topic more explicit. Finally, in response to determining that the verification result characterizes the text content in the topic text as correct, the aforementioned topic circulation effect text is displayed on the target terminal. In summary, by using a predetermined set of topic parameter indicators, the popularity of an input topic can be accurately predicted. Furthermore, by using at least one standard information corresponding to at least one topic comment direction, a topic flow effect text with a more explicit topic flow can be accurately generated. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0015] Figure 1 is a flowchart of some embodiments of the text display method according to the present disclosure;

[0016] Figure 2 is a schematic structural diagram of some embodiments of the text display device according to the present disclosure;

[0017] Figure 3 It is a structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0018] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0019] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.

[0020] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0021] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0022] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0023] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0024] refer to Figure 1 , shows a process 100 of some embodiments of the text display method according to the present disclosure. The text display method includes the following steps:

[0025] Step 101: Acquire an input topic input by a target user on a target input page and a material file corresponding to the input topic.

[0026] In some embodiments, the execution entity of the above-described text display method (e.g., an electronic device) can obtain, via a wired or wireless connection, the input topic entered by a target user on a target input page and the corresponding material files. The target user may be the user who initiated the topic. The input topic may be a topic initiated by the user on a corresponding topic circulation platform. The topic circulation platform may be a platform that supports topic discussions within a certain scale. For example, the topic circulation platform may be a news event discussion platform or a public opinion video discussion platform. For example, the input topic may be "Is the weather in Beijing, Tianjin, and Hebei too hot today?" The material files may be files related to the input topic in terms of topic content. The material files may be files in various modalities. For example, various modalities may include text, image, video, and audio. For example, the material files may be supporting documents that support the viewpoints of the input topic, flowcharts of the content corresponding to the input topic, or entertaining illustrations of the content corresponding to the input topic. The specific file contents of the specific material files will not be detailed here. The target input page may be a page that supports the target user in entering topics and material files. In practice, the target input page supports multimodal input. For example, if the input topic is in audio format, the target input page can support audio input to the target input page. The audio is then converted to text through the target input page.

[0027] Step 102: semantically split the input topic and the material file to obtain first semantic split information and second semantic split information.

[0028] In some embodiments, the execution subject may perform semantic splitting on the input topic and the material file to obtain first semantic splitting information and second semantic splitting information. The first semantic splitting information may be the information after the input topic is semantically split. The material file may be the information after the input topic is semantically split. In practice, the first semantic splitting information may include: name information, place name information, organizational information, entity relationship information, event type information, event time information, emotional information, and semantic role information. Similarly, the second semantic splitting information includes the same type of information as the first semantic splitting information.

[0029] Step 103 : Aggregate the first semantic splitting information and the second semantic splitting information to generate semantic splitting summary information.

[0030] In some embodiments, the execution entity may aggregate the first semantic splitting information and the second semantic splitting information to generate semantic splitting aggregate information, wherein the semantic splitting aggregate information may be information obtained by aggregating the semantic information.

[0031] As an example, the execution entity may fuse the first semantic split information and the second semantic split information under the same information type to generate respective fused information under respective information types as the semantic split summary information.

[0032] Step 104 , based on the semantic split summary information, using the available computing resources corresponding to the central processing unit and / or the available computing resources corresponding to the image processor, determines the topic indicator content under the predetermined topic parameter indicator set to obtain a topic indicator content set.

[0033] In some embodiments, the execution subject may determine the topic indicator content under the predetermined topic parameter indicator set based on the semantic splitting and summary information, using the available computing resources corresponding to the central processing unit (CPU) and / or the available computing resources corresponding to the graphics processing unit (GPU), to obtain a topic indicator content set. The above-mentioned topic indicator content set is a content set for each topic circulation platform. The predetermined topic parameter indicator may be a pre-set parameter indicator related to topic circulation. The topic indicator content may be the indicator content corresponding to the predetermined topic parameter indicator in the current topic scenario. The platforms in each topic circulation platform are different. The available computing resources may be available online computing resources.

[0034] In some optional implementations of some embodiments, the above-mentioned semantic splitting and summary information, using available computing resources corresponding to the central processing unit and / or available computing resources corresponding to the image processor, to determine the topic indicator content under the predetermined topic parameter indicator set, and obtaining the topic indicator content set may include the following steps:

[0035] In the first step, estimated computational resources corresponding to the topic indicator content set are generated based on the information volume corresponding to the semantic split summary information and the computational workload corresponding to each predetermined topic parameter indicator in the predetermined topic parameter indicator set. The estimated computational resources may represent the computational resources required to generate the topic indicator content set. The estimated computational resources may be determined based on the total computational workload corresponding to the estimated topic parameter indicator set.

[0036] In the second step, in response to determining that the available computing resources corresponding to the above-mentioned central processing unit are greater than the above-mentioned estimated computing resources, based on the above-mentioned semantic splitting and summary information, the available computing resources corresponding to the central processing unit are used to determine the topic indicator content under the predetermined topic parameter indicator set to obtain the topic indicator content set.

[0037] In the third step, in response to determining that the available computing resources corresponding to the above-mentioned central processing unit are less than the above-mentioned estimated computing resources and the available computing resources corresponding to the target image processor are greater than the above-mentioned estimated computing amount, based on the above-mentioned semantic splitting summary information, the available computing resources corresponding to the above-mentioned target image processor are used to determine the topic indicator content under the predetermined topic parameter indicator set to obtain the topic indicator content set.

[0038] In the fourth step, in response to determining that the available computing resources corresponding to the above-mentioned central processing unit are less than the above-mentioned estimated computing resources and the available computing resources corresponding to any image processor are less than the above-mentioned estimated computing amount, based on the above-mentioned semantic splitting summary information, the available computing resources corresponding to each distributed image processor are utilized to determine the topic indicator content under the predetermined topic parameter indicator set to obtain the topic indicator content set.

[0039] In some optional implementations of some embodiments, the semantic segmentation summary information includes: behavior noun information, event noun information, person noun information, organization noun information, media noun information, and place noun information. The predetermined topic parameter indicator set includes: topic lift average slope indicator, topic subject reputation indicator, first topic traffic indicator, topic subject awareness indicator, second topic traffic indicator, estimated topic traffic indicator, communication meta-score indicator, and topic subject attitude indicator. The topic lift average slope indicator can represent the trend of topic flow. A larger value for the topic lift average slope indicator indicates a greater trend in topic flow. In practice, topic flow can be topic diffusion. The topic subject reputation indicator can represent the reputation of the topic originator. A larger value for the topic subject reputation indicator indicates a better reputation for the topic originator. In practice, the reputation of the topic originator can be determined by obtaining posted comments and reply information corresponding to the topic originator. The first topic traffic indicator can represent the median topic traffic within a topic diffusion environment. The topic diffusion environment can be any environment where topic diffusion occurs, for example, various platforms for topic diffusion. The topic subject popularity index can represent the popularity of the topic issuer. The larger the corresponding indicator value of the topic subject popularity index, the higher the popularity of the topic issuer. The second topic flow index can represent the total topic flow in the topic propagation environment. That is, the second topic flow index can be the total flow of each topic in the topic propagation environment. The estimated topic flow index can represent the total flow of the evaluated topic in each time period. The evaluated topic can be a topic that is evaluated by the prediction of the topic flow effect. The various time periods can be pre-set time periods. The specific setting method will not be repeated here. The communication meta-rating index can represent the rating of the communication meta-lexicon. The topic subject attitude index can represent the public's attitude towards the topic issuer. The higher the corresponding indicator value of the topic subject attitude index, the better the public's attitude towards the topic issuer.

[0040] In practice, communication meta-vocabulary can be classified and graded. Communication meta-scores can be determined based on the categories and levels corresponding to the communication meta-vocabulary. Each communication meta-vocabulary has a corresponding lexical category. Specifically, the Large Language Model (LLM) model can be used to implement lexical classification and grading. For example, communication meta-vocabulary can be lexical items of corresponding levels under Category 1, or lexical items of corresponding levels under Category 2, and so on. Specific categories include, but are not limited to, Category 1 and Category 2. Specifically, Category 1 can be related to "survival and safety risks." Category 1 has five levels of lexical classification. The first level under Category 1 can be lexical items related to "culture and philosophy." For example, lexical items related to "culture and philosophy" can include, but are not limited to, at least one of the following: traditional customs, etiquette, festivals, spiritual sustenance, philosophy of life, cultural integration, philosophical enlightenment, and moral exploration. The second level under Category 1 can be lexical items related to "social and environmental context." For example, lexical items related to "social and environmental context" can include, but are not limited to, at least one of the following: community support, family, friendship, environmental adaptation, climate change, social stability, environmental protection, and ecological restoration. The third level under Category 1 may include terms related to "survival strategies and risk management." For example, terms related to "survival strategies and risk management" may include, but are not limited to, at least one of the following: resource acquisition, wealth, income, risk management, self-defense, emergency preparedness, successful investment, safety measures, job hunting, career development, and workplace skills. The fourth level under Category 1 may include terms related to "survival-related items and behaviors." For example, terms related to "survival-related items and behaviors" may include, but are not limited to, at least one of the following: food, water, shelter, natural disasters, war, accidents, poison, chemicals, distress, rescue, escape, and survival. The fifth level under Category 1 may include terms related to "survival and safety risks." For example, terms related to "survival and safety risks" may include, but are not limited to, at least one of the following: survival, safety, health, death, danger, fatality, extinction, survival, and recovery. Specifically, Category 2 may be a category related to "reproduction." Category 2 has five levels of vocabulary classification. The first level under Category 2 may include terms related to "human evolution and environmental factors." For example, the vocabulary associated with "human evolution and environmental factors" could include, but is not limited to, at least one of the following: ecosystem, environmental protection, climate change, ecological balance, and sustainable development. The second level under Category 2 could include vocabulary associated with "reproductive rights and social and cultural influences." For example, the vocabulary associated with "reproductive rights and social and cultural influences" could include, but is not limited to, at least one of the following: reproductive rights, celebrities, idols, second child, and social roles. The third level under Category 2 could include vocabulary associated with "reproductive health and rights."For example, the various words related to "reproductive health and rights" may include, but are not limited to, at least one of the following: reproductive health, maternal and child health, reproductive rights, fertility policy, and reproductive technology. The fourth level under Category 2 may be various words related to "reproductive behavior." For example, the various words related to "reproductive behavior" may include, but are not limited to, at least one of the following: pregnancy, childbirth, reproduction, marriage, personal relationships, family harmony, successful children's education, and family. The fifth level under Category 2 may be various words related to "sexual behavior." For example, the various words related to "sexual behavior" may include, but are not limited to, at least one of the following: long legs, genetics, sexual behavior, muscles, and charm.

[0041] Here, the communication meta-score can be the value obtained by multiplying the corresponding level of the communication meta-word by the category coefficient corresponding to the category to which it belongs. In other words, each category has a corresponding category coefficient. For example, the category coefficient corresponding to category 1 is the value "1". The category coefficient corresponding to category 2 is the value "1.2".

[0042] Optionally, the above-mentioned semantic splitting and summary information is used to determine the topic indicator content under the predetermined topic parameter indicator set using the available computing resources corresponding to the central processing unit, and obtain the topic indicator content set, including:

[0043] Using the available computing resources corresponding to the central processing unit, perform the following determination steps:

[0044] The first step is to determine the average slope of topic improvement corresponding to the above-mentioned average slope indicator of topic improvement.

[0045] The second step is to determine the topic subject reputation value corresponding to the above topic subject reputation indicator based on the above semantic splitting and summary information.

[0046] As an example, the execution entity can first use the APIs of various social and search platforms or web crawlers to crawl the topic bodies in the semantically segmented summary information, obtaining comments and replies to the disseminator (i.e., the target user) under the corresponding topic. Then, LLM is used to evaluate the semantic tone of each sentence, distinguishing between positive and negative sentences, and subtracting the number of negative sentences from the number of positive sentences to obtain the word-of-mouth value.

[0047] The third step is to determine the median of the topic traffic corresponding to the first topic traffic indicator, wherein the first topic traffic indicator corresponds to each topic at the current time.

[0048] As an example, first, we use the APIs of various social and search platforms or crawlers to obtain traffic trends for the top topics. Next, we record the hourly traffic values ​​for each topic to create a traffic value sequence. Next, we select the median from this traffic value sequence and use it as the median topic traffic value.

[0049] The fourth step is to determine the topic subject popularity corresponding to the above topic subject popularity index based on the above semantic splitting and summary information.

[0050] As an example, first, we use the APIs or web crawlers of various social and search platforms to crawl the semantically segmented summary information to obtain the number of likes and followers for posts published by the subject of the topic. Then, based on these likes and followers, we use the popularity association table to determine the topic subject's popularity corresponding to the aforementioned topic subject popularity indicator.

[0051] The fifth step is to determine the total topic traffic corresponding to the second topic traffic indicator. The second topic traffic indicator corresponds to each topic at the current time. The topics can be topics on the popularity list of each platform.

[0052] As an example, the total traffic value of the topics on the list is obtained by using the APIs of various social platforms and search platforms or crawling with web worms, and the total traffic value is obtained by summing them up.

[0053] In the sixth step, based on the semantic splitting and summary information, a set of estimated topic traffic amounts corresponding to the estimated topic traffic indicators is determined, wherein the estimated topic traffic indicators correspond to the input topics.

[0054] As an example, the topic information in the semantic split summary information is crawled using the APIs of various social platforms and search platforms or network worms to obtain the total value of the evaluated traffic, and the sum is calculated to obtain the total traffic of the evaluated topic.

[0055] Step 7: Determine the communication meta-score corresponding to the communication meta-score indicator based on the semantic split summary information.

[0056] As an example, the execution entity may determine a classification and grading table corresponding to the communication meta-vocabulary. Then, the execution entity may determine the vocabulary associated with the communication meta-vocabulary in the semantic segmentation summary information to obtain at least one vocabulary. Next, at least one score corresponding to the at least one vocabulary is retrieved from the classification and grading table. Finally, the scores in the at least one score are summed to generate a communication meta-score.

[0057] The eighth step is to determine the topic subject attitude value corresponding to the above topic subject attitude indicator.

[0058] As an example, we first use the APIs or web crawlers of various social and search platforms to crawl the comment sets of articles published by the subject of the topic in the semantic segmentation summary information. Then, we perform sentiment semantic analysis on each comment in the comment set to generate a sentiment semantic analysis information set. Based on this sentiment semantic analysis information set, we determine the attitude tendency value of the subject of the topic, which serves as the subject's attitude value.

[0059] In the ninth step, the average slope of the topic improvement, the reputation value of the topic subject, the median of the topic traffic, the popularity of the topic subject, the total amount of topic traffic, the estimated total amount of topic traffic, the communication meta-score and the attitude value of the topic subject are determined as the topic indicator content set.

[0060] Optionally, the above-mentioned determination of the topic improvement average slope corresponding to the above-mentioned topic improvement average slope indicator based on the above-mentioned semantic splitting summary information may include the following steps:

[0061] The first step is to determine the maximum value of topic traffic, the starting value of topic traffic, the ending value of topic traffic, the first time point corresponding to the maximum value of topic traffic, the second time point corresponding to the starting value of topic traffic, and the third time point corresponding to the ending value of topic traffic based on the above semantic splitting and summary information. The maximum value of topic traffic can be the maximum value of topic traffic for each topic on the list. The starting value of topic traffic can be the starting value of topic traffic for each topic on the list. The ending value of topic traffic can be the extinction point value of topic traffic for each topic on the list. The first time point can be the time point when the maximum value of topic traffic appears. The second time point can be the time point when the starting value of topic traffic appears. The third time point can be the time point when the ending value of topic traffic appears.

[0062] The second step is to determine the time difference between the first time point and the second time point as the first time difference.

[0063] The third step is to subtract the starting value of the topic traffic from the maximum value of the topic traffic to obtain a second subtraction value.

[0064] In the fourth step, the second subtraction value is divided by the first time difference to generate a third division value.

[0065] Step 5: Subtract the topic traffic end value from the topic traffic maximum value to obtain a third subtraction value.

[0066] Step 6: Determine the time difference between the first time point and the third time point as the second time difference.

[0067] In the seventh step, the third subtraction value is divided by the second time difference to obtain a fourth division value.

[0068] In the eighth step, the third division value is added to the fourth division value to obtain a third added value.

[0069] In the ninth step, the third added value is divided by the third predetermined value to generate a fifth divided value as the topic improvement average slope.

[0070] Optionally, the above-mentioned determination of the topic subject attitude value corresponding to the above-mentioned topic subject attitude indicator may include the following steps:

[0071] The first step is to obtain a topic reply information set corresponding to the input topic, wherein the topic reply information can be information replied to the input topic.

[0072] The second step is to determine the image set corresponding to the above topic reply information set, wherein the image set can be each image that appears in the topic reply information set.

[0073] In the third step, for each image in the above image set, perform the following first generation step:

[0074] Sub-step 1: determining the image occurrence frequency corresponding to the above image, wherein the image occurrence frequency may be the number of times the image appears.

[0075] Sub-step 2: determining at least one topic reply text message corresponding to the image, wherein the at least one topic reply text message may be individual reply texts replying to the corresponding image.

[0076] Sub-step 3: determining the text emotion information corresponding to each topic reply text message in the at least one topic reply text message, and obtaining at least one text emotion information.

[0077] As an example, the execution entity may utilize a sentiment analysis model to determine text sentiment information corresponding to each topic reply text message in the at least one topic reply text message, and obtain at least one text sentiment information.

[0078] Sub-step 4: setting image emotion tendency information for the above image based on the above at least one text emotion information.

[0079] As an example, first, the execution subject may calculate the number of information on each emotional tendency in at least one text emotional information to obtain the number of each information. Then, the emotional tendency with the highest number of corresponding information on each emotional tendency is selected as the image emotional tendency information.

[0080] The fourth step is to determine the topic reply text information set corresponding to the above topic reply information set, wherein the topic reply information in the topic reply information set corresponds one to one to the topic reply text information in the topic reply text information set.

[0081] Step 5: For each topic reply message in the above topic reply message set, execute the following second generation step:

[0082] Sub-step 1: determining the topic reply text information corresponding to the above topic reply information as the target topic reply text information.

[0083] Sub-step 2: determining the text sentiment information corresponding to the target topic reply text information as the target text sentiment information.

[0084] Sub-step 3: performing sentiment vocabulary extraction processing on the target topic reply text information to generate at least one sentiment word.

[0085] Sub-step 4, determining at least one word sentiment tendency information corresponding to the above-mentioned at least one sentiment word. There is a one-to-one correspondence between the sentiment word in the at least one sentiment word and the word sentiment tendency information in the at least one word sentiment tendency information. The word sentiment tendency information can characterize the sentiment tendency of the vocabulary. For example, the word sentiment tendency information can be but not limited to at least one of the following: negative tendency information, positive tendency information, neutral tendency information. Each type of word sentiment tendency information can be set with a corresponding tendency value. For example, the tendency value corresponding to the negative tendency information can be the value "2". The tendency value corresponding to the positive tendency information can be the value "1". The tendency value corresponding to the neutral tendency information can be the value "0.5". Sub-step 5, determining the image sentiment tendency information corresponding to the above-mentioned target topic reply information as the target image sentiment tendency information.

[0086] Sub-step 6: Generate emotional tendency summary information corresponding to the above-mentioned topic reply information based on the above-mentioned target text emotional information, the above-mentioned at least one word emotional information and the above-mentioned target image emotional tendency information.

[0087] The sixth step is to generate the above-mentioned topic subject attitude value based on the obtained emotional tendency summary information set.

[0088] As an example, first, each sentiment tendency summary information in the sentiment tendency summary information set is digitized to generate a sentiment value, thereby obtaining a sentiment value set. Then, each sentiment value in the sentiment value set is added together to generate a topic subject attitude value.

[0089] The aforementioned "optional" feature, as one of the inventive aspects of this disclosure, addresses the technical issue mentioned in the background art: "the generated topic subject attitude values ​​are not accurate enough." Based on this, this disclosure accurately generates topic subject attitude values ​​by performing multimodal, precise analysis of the image and text content corresponding to topic replies.

[0090] Step 105: Generate topic popularity information for the input topic based on the topic index content set.

[0091] In some embodiments, the execution entity may generate topic popularity information for the input topic based on the topic indicator content set. The topic popularity information may represent the popularity (i.e., the degree of spread) of the input topic. In practice, the topic popularity information may be the topic heat corresponding to the input topic. The topic popularity information may be information in numerical form. The larger the numerical value, the higher the topic heat representing the input topic.

[0092] Optionally, generating the topic popularity information for the input topic based on the topic indicator content set may include the following steps:

[0093] In the first step, the estimated total topic traffic amounts in the above-mentioned estimated total topic traffic amount set are added together to obtain a first added value.

[0094] In the second step, the median of the topic traffic is subtracted from the first added value to generate a first subtracted value.

[0095] The third step is to multiply the first subtraction value by the average slope of topic improvement to generate a first multiplication value.

[0096] The fourth step is to perform negation processing on the first multiplication value to generate a negated multiplication value.

[0097] In the fifth step, the multiplied value after negation is used as the exponent and the first predetermined value is used as the base to generate a first exponential value.

[0098] In the sixth step, the first index value is added to the second predetermined value to obtain a second added value.

[0099] Step 7: Divide the total topic traffic by the second added value to obtain the first divided value.

[0100] In the eighth step, the communication element score is divided by a third predetermined value to obtain a second division value.

[0101] In the ninth step, the second division value is used as an exponent and the first predetermined value is used as a base to generate a second exponent value.

[0102] In the tenth step, the second division value is multiplied by the second exponent value to obtain a second multiplied value.

[0103] In the eleventh step, the popularity of the topic subject is multiplied by the attitude value of the topic subject to obtain a third multiplied value.

[0104] The twelfth step is to perform absolute value processing on the reputation value of the above topic subject to generate the absolute value of the reputation.

[0105] In the thirteenth step, the third multiplication value is multiplied by the absolute value of the word-of-mouth to generate topic popularity information.

[0106] Step 106: Obtain the topic review standard file.

[0107] In some embodiments, the execution entity may obtain a topic review standard file. The topic review standard file includes: at least one standard information corresponding to at least one topic review direction. The at least one topic review direction may be a pre-set topic review direction. The topic review direction may be a review angle for reviewing a topic. There is a one-to-one correspondence between the standard information in the at least one standard information and the topic review direction in the at least one topic review direction. The standard information may be a judgment rule for the topic review direction.

[0108] Step 107 : Based on the topic popularity information and the topic review standard file, a pre-trained large language model is used to generate a topic circulation effect text in a target format corresponding to the input topic.

[0109] In some embodiments, the execution entity may utilize a pre-trained large language model based on the topic popularity information and the topic comment standard file to generate a topic circulation effect text in a target format corresponding to the input topic. In practice, the large language model may be an LLM model. The model type corresponding to the large language model is selected by the target user in the topic circulation effect text generation page. That is, in practice, the large language model may select a model structure in the topic circulation effect text generation page.

[0110] In some optional implementations of some embodiments, the above-mentioned topic review standard files include: topic popularity review standard source file, target user review standard source file, heat review standard source file, word-of-mouth assignment review standard source file, word-of-mouth evaluation review standard source file, and place name heat review standard source file. The topic popularity review standard source file can be a source file for making a standard review of topic popularity. The target user review standard source file can be a source file for making a standard review of user-related situations. The heat review standard source file can be a source file for making a standard review of topic heat. The word-of-mouth assignment review standard source file can be a standard source file for how to assign a value to word of mouth. The place name heat review standard source file can be a source file for making a standard review of place name heat.

[0111] Optionally, the step of generating the topic popularity effect text in the target format corresponding to the input topic using a pre-trained large language model based on the topic popularity information and the topic review standard file may include the following steps:

[0112] The first step is to combine the source file contents of the above-mentioned topic popularity evaluation standard source file, the above-mentioned target user evaluation standard source file, the above-mentioned heat evaluation standard source file, the above-mentioned word-of-mouth assignment evaluation standard source file, the above-mentioned word-of-mouth evaluation standard source file and the above-mentioned place name heat evaluation standard source file to generate a combined source file.

[0113] In the second step, the combined source file and the topic popularity information are input into the large language model to generate the topic spread effect text.

[0114] Step 108: Verify the text content of the above topic spread effect text to generate a verification result.

[0115] In some embodiments, the execution entity may perform text content verification on the topic spread effect text to generate a verification result.

[0116] In some optional implementations of some embodiments, performing text content verification on the topic spread effect text to generate a verification result may include the following steps:

[0117] In the first step, for each topic review direction in the at least one topic review direction, the following third generation step is performed:

[0118] Sub-step 1: using at least one pre-trained sentiment analysis neural network model, determine the first topic review content corresponding to the above topic review direction.

[0119] Sub-step 2: determining the second topic review content in the topic flow effect text corresponding to the topic review direction.

[0120] Sub-step 3: determining the content difference between the review content of the first topic and the review content of the second topic.

[0121] In the second step, the verification result is generated based on the obtained at least one content difference.

[0122] Step 109 , in response to determining that the verification result indicates that the text content in the topic text is correct, the topic circulation effect text is multimodally displayed on the target terminal.

[0123] In some embodiments, in response to determining that the verification result indicates that the text content in the topic text is correct, the execution entity may perform a multimodal display of the topic flow effect text on the target terminal. The multimodal display may include: text mode display and image mode display. In the case of image mode display, the topic flow effect text needs to be converted into an image.

[0124] The above-described embodiments of the present disclosure have the following beneficial effects: The text display methods of some embodiments of the present disclosure are applicable to different topic flow platforms, accurately and efficiently generating topic flow text corresponding to an input topic while fully utilizing current computing resources, effectively displaying the corresponding input topic flow effect in a multimodal manner. Specifically, the reasons for the inaccurate flow effect of the input topic and the irrational allocation of computing resources are: the use of fixed algorithm models in cross-platform and cross-cultural scenarios has the problem of poor adaptability for predicting topic flow effects, resulting in inaccurate prediction of topic flow effects. In addition, the use of neural networks to predict topic flow effects often suffers from irrational scheduling of computing resources, resulting in significant delays and lags in the generation of topic flow effects. Based on this, the text display methods of some embodiments of the present disclosure first obtain the input topic entered by the target user on the target input page and the corresponding material file of the input topic, so as to facilitate the subsequent prediction and display of the topic flow effect for the input topic. The material files include: image-formatted material files, video-formatted material files, and text-formatted material files. In addition, by inputting on the target input page, it is possible to facilitate the uploading of topic-related information of the target user. Then, the above-mentioned input topic and the above-mentioned material file are semantically split to obtain first semantic splitting information and second semantic splitting information, so as to facilitate semantic extraction of the input topic and material file at a semantic granularity. At the same time, it is also convenient for the subsequent determination of the indicator content set corresponding to the topic parameter indicator set. Secondly, based on the above-mentioned semantic splitting summary information, the available computing resources corresponding to the central processing unit and / or the available computing resources corresponding to the image processor can be used to accurately determine the topic indicator content under the predetermined topic parameter indicator set to obtain the topic indicator content set. Among them, the above-mentioned topic indicator content set is a content set for each topic circulation platform. The obtained topic indicator content set is used for the subsequent generation of topic popularity information. Moreover, by considering the amount of available computing resources corresponding to the central processing unit and the amount of available computing resources corresponding to the image processor, the corresponding computing resources can be prepared in advance for the generation of the topic indicator content set, avoiding the problem of large delays and freezes in the generation of topic flow effects.

[0125] Next, based on the aforementioned topic indicator content set, topic popularity information for the input topic can be accurately generated. Accurately generating this topic popularity information can characterize the corresponding circulation heat of the input topic. Next, a topic review standard file is obtained to facilitate subsequent evaluation of topic circulation for at least one topic review direction, thereby generating a more accurate topic circulation effect text. The aforementioned topic review standard file includes at least one standard information corresponding to at least one topic review direction. Furthermore, based on the aforementioned topic popularity information and the aforementioned topic review standard file, a pre-trained large language model can be utilized to accurately generate a topic circulation effect text in a target format corresponding to the input topic. The large language model corresponds to the model type selected by the target user on the topic circulation effect text generation page. The generated topic circulation effect text can fully demonstrate the circulation effect of the input topic under each topic review direction, making the circulation effect of the input topic more explicit. Finally, in response to determining that the verification result characterizes the text content in the topic text as correct, the aforementioned topic circulation effect text is displayed on the target terminal. In summary, by using a predetermined set of topic parameter indicators, the popularity of an input topic can be accurately predicted. Furthermore, by using at least one standard information corresponding to at least one topic comment direction, a topic flow effect text with a more explicit topic flow can be accurately generated.

[0126] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a text display device. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the text display device can be specifically applied to various electronic devices.

[0127] like Figure 2As shown, a text display device 200 includes: a first acquisition unit 201, a semantic splitting unit 202, an information aggregation unit 203, a determination unit 204, a first generation unit 205, a second acquisition unit 206, a second generation unit 207, a verification unit 208 and a display unit 209. Among them, the first acquisition unit 201 is configured to obtain the input topic input by the target user on the target input page and the material file corresponding to the above input topic, wherein the above material file includes: material file in image form, material file in video form and material file in text form; the semantic splitting unit 202 is configured to perform semantic splitting on the above input topic and the above material file to obtain first semantic splitting information and second semantic splitting information; the information aggregation unit 203 is configured to aggregate the above first semantic splitting information and the above second semantic splitting information to generate semantic splitting aggregation information; the determination unit 204 is configured to determine the topic indicator content under the predetermined topic parameter indicator set based on the above semantic splitting aggregation information using the available computing resources corresponding to the central processing unit and / or the available computing resources corresponding to the image processor to obtain a topic indicator content set, wherein the above topic indicator content set is a content set for each topic circulation platform; the first generation unit 201 is configured to perform semantic splitting on the above input topic and the above material file to obtain first semantic splitting information and second semantic splitting information; the information aggregation unit 203 is configured to aggregate the above first semantic splitting information and the above second semantic splitting information to generate semantic splitting aggregation information; the determination unit 204 is configured to determine the topic indicator content under the predetermined topic parameter indicator set based on the above semantic splitting aggregation information using the available computing resources corresponding to the central processing unit and / or the available computing resources corresponding to the image processor to obtain a topic indicator content set, wherein the above topic indicator content set is a content set for each topic circulation platform; The generating unit 205 is configured to generate the topic popularity information for the input topic according to the above-mentioned topic indicator content set; the second acquiring unit 206 is configured to acquire the topic review standard file, wherein the above-mentioned topic review standard file includes: at least one standard information corresponding to at least one topic review direction; the second generating unit 207 is configured to generate the topic circulation effect text in the target format corresponding to the input topic according to the above-mentioned topic popularity information and the above-mentioned topic review standard file using a pre-trained large language model, wherein the model type corresponding to the above-mentioned large language model is the model type selected by the above-mentioned target user in the topic circulation effect text generation page; the verifying unit 208 is configured to perform text content verification on the above-mentioned topic circulation effect text to generate a verification result; the display unit 209 is configured to perform multimodal display on the target terminal in response to determining that the above-mentioned verification result represents that the text content in the topic text is correct.

[0128] It is understandable that the units recorded in the text display device 200 and the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the text display device 200 and the units included therein, and will not be repeated here.

[0129] Reference below Figure 3 , which shows a structural schematic diagram of an electronic device (eg, an electronic device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0130] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. Various programs and data required for the operation of the electronic device 300 are also stored in the RAM 303. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0131] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0132] In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from a network via the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are performed.

[0133] It should be noted that in some embodiments of the present disclosure, the computer-readable medium mentioned above may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0134] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0135] The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist independently and not be assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: obtains the input topic input by the target user on the target input page and the material file corresponding to the above-mentioned input topic, wherein the above-mentioned material file includes: material file in image form, material file in video form and material file in text form; performs semantic splitting on the above-mentioned input topic and the above-mentioned material file to obtain first semantic splitting information and second semantic splitting information; summarizes the above-mentioned first semantic splitting information and the above-mentioned second semantic splitting information to generate semantic splitting summary information; according to the above-mentioned semantic splitting summary information, utilizes the available computing resources corresponding to the central processing unit and / or the available computing resources corresponding to the image processor to determine the topic indicator content under the predetermined topic parameter indicator set to obtain a topic indicator content set, wherein The above-mentioned topic indicator content set is a content set for each topic circulation platform; based on the above-mentioned topic indicator content set, the topic popularity information for the above-mentioned input topic is generated; a topic review standard file is obtained, wherein the above-mentioned topic review standard file includes: at least one standard information corresponding to at least one topic review direction; based on the above-mentioned topic popularity information and the above-mentioned topic review standard file, a pre-trained large language model is used to generate a topic circulation effect text in a target format corresponding to the above-mentioned input topic, wherein the model type corresponding to the above-mentioned large language model is the model type selected by the above-mentioned target user in the topic circulation effect text generation page; the text content of the above-mentioned topic circulation effect text is verified to generate a verification result; in response to determining that the above-mentioned verification result represents that the text content in the topic text is correct, the above-mentioned topic circulation effect text is multimodally displayed on the target terminal.

[0136] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0137] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0138] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be provided in a processor, for example, may be described as: a processor comprising a first acquisition unit, a semantic splitting unit, an information aggregation unit, a determination unit, a first generation unit, a second acquisition unit, a second generation unit, a verification unit and a display unit. Among them, the names of these units do not constitute a limitation on the unit itself under certain circumstances. For example, the first acquisition unit may also be described as a "unit for acquiring the input topic corresponding to the target user and the material file corresponding to the above-mentioned input topic."

[0139] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0140] The above description is only an illustration of some preferred embodiments of the present disclosure and the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A text display method, comprising: Acquire an input topic input by a target user on a target input page and a material file corresponding to the input topic, wherein the material file includes: a material file in the form of an image, a material file in the form of a video, and a material file in the form of a text; Performing semantic splitting on the input topic and the material file to obtain first semantic splitting information and second semantic splitting information; Aggregating the first semantic splitting information and the second semantic splitting information to generate semantic splitting summary information; According to the semantic split summary information, the topic indicator content under the predetermined topic parameter indicator set is determined by utilizing the available computing resources corresponding to the central processing unit and / or the available computing resources corresponding to the image processing unit to obtain a topic indicator content set, wherein the topic indicator content set is a content set for each topic circulation platform, and the predetermined topic parameter indicator set includes: a topic promotion average slope indicator, a topic subject word-of-mouth indicator, a first topic flow indicator, a topic subject popularity indicator, a second topic flow indicator, an estimated topic flow indicator, a communication meta-score indicator, and a topic subject attitude indicator, wherein determining the topic subject attitude value corresponding to the topic subject attitude indicator includes: Obtaining a topic reply information set corresponding to the input topic, wherein the topic reply information may be information replied to the input topic; Determining an image set corresponding to the topic reply information set, wherein the image set is each image that appears in the topic reply information set; For each image in the image set, the following first generation step is performed: Determining an image occurrence frequency corresponding to the image, wherein the image occurrence frequency is the number of times the image appears; Determining at least one topic reply text message corresponding to the image, wherein the at least one topic reply text message is a reply text corresponding to the image; Determining text emotion information corresponding to each topic reply text message in the at least one topic reply text message to obtain at least one text emotion information; Setting image emotional tendency information for the image based on the at least one text emotional information, wherein the number of information on each emotional tendency in the at least one text emotional information is obtained to obtain the number of each information, and selecting the emotional tendency with the highest number of corresponding information on each emotional tendency as the image emotional tendency information; Determining a topic reply text information set corresponding to the topic reply information set, wherein the topic reply information in the topic reply information set and the topic reply text information in the topic reply text information set have a one-to-one correspondence; For each topic reply information in the topic reply information set, the following second generation step is performed: Determine the topic reply text information corresponding to the topic reply information as the target topic reply text information; Determining text sentiment information corresponding to the target topic reply text information as target text sentiment information; Performing sentiment vocabulary extraction processing on the target topic reply text information to generate at least one sentiment word; Determining at least one word sentiment tendency information corresponding to the at least one sentiment word, wherein there is a one-to-one correspondence between the sentiment word in the at least one sentiment word and the word sentiment tendency information in the at least one word sentiment tendency information, the word sentiment tendency information can represent the sentiment tendency of the vocabulary, and each type of word sentiment tendency information can be set with a corresponding tendency value; Determining the image sentiment tendency information corresponding to the target topic reply information as the target image sentiment tendency information; Generate emotional tendency summary information corresponding to the topic reply information based on the target text emotional information, the at least one word emotional information and the target image emotional tendency information; Generating the topic subject attitude value based on the obtained emotional tendency summary information set, wherein each emotional tendency summary information in the emotional tendency summary information set is digitized to generate an emotional value to obtain an emotional value set, and each emotional value in the emotional value set is added to generate the topic subject attitude value; Generate topic popularity information for the input topic based on the topic indicator content set, wherein the topic indicator content in the topic indicator content set includes a topic subject attitude value; obtain a topic review standard file, wherein the topic review standard file includes: at least one standard information corresponding to at least one topic review direction; Generate a topic popularity effect text in a target format corresponding to the input topic using a pre-trained large language model based on the topic popularity information and the topic comment standard file, wherein the large language model corresponds to the model type selected by the target user on the topic popularity effect text generation page; The topic circulation effect text is verified for text content to generate a verification result; in response to determining that the verification result indicates that the text content in the topic text is correct, the topic circulation effect text is multimodally displayed on the target terminal.

2. The method according to claim 1, wherein The method of determining the topic indicator content under a predetermined topic parameter indicator set based on the semantic splitting and summary information and utilizing available computing resources corresponding to a central processing unit and / or available computing resources corresponding to an image processing unit to obtain a topic indicator content set includes: Generate an estimated computing resource corresponding to the topic indicator content set according to the information amount corresponding to the semantic split summary information and the computing amount corresponding to each predetermined topic parameter indicator in the predetermined topic parameter indicator set; In response to determining that the available computing resources corresponding to the central processing unit are greater than the estimated computing resources, determining topic indicator content under a predetermined topic parameter indicator set using the available computing resources corresponding to the central processing unit according to the semantic split summary information, to obtain a topic indicator content set; In response to determining that the available computing resources corresponding to the central processing unit are less than the estimated computing resources and the available computing resources corresponding to the target image processor are greater than the estimated computing amount, determining topic indicator content under a predetermined topic parameter indicator set using the available computing resources corresponding to the target image processor according to the semantic splitting summary information to obtain a topic indicator content set; In response to determining that the available computing resources corresponding to the central processing unit are less than the estimated computing resources and the available computing resources corresponding to any image processor are less than the estimated computing amount, based on the semantic splitting summary information, the available computing resources corresponding to each distributed image processor are utilized to determine the topic indicator content under the predetermined topic parameter indicator set to obtain a topic indicator content set.

3. The method according to claim 2, wherein: The semantic split summary information includes: behavior noun information, event noun information, person noun information, organization noun information, media noun information and place noun information; and The method of determining the topic indicator content under a predetermined topic parameter indicator set based on the semantic splitting and summarizing information and utilizing the available computing resources corresponding to the central processing unit to obtain a topic indicator content set includes: Using the available computing resources corresponding to the central processing unit, perform the following determination steps: Determine the topic improvement average slope corresponding to the topic improvement average slope indicator; Determining the topic subject reputation value corresponding to the topic subject reputation indicator according to the semantic split summary information; Determine a median of topic traffic corresponding to the first topic traffic indicator, wherein the first topic traffic indicator corresponds to each topic at the current time; Determining the topic subject popularity corresponding to the topic subject popularity index according to the semantic split summary information; Determine the total amount of topic traffic corresponding to the second topic traffic indicator, wherein the second topic traffic indicator corresponds to each topic at the current time; Determining, based on the semantic split summary information, a set of estimated topic traffic amounts corresponding to the estimated topic traffic indicators, wherein the estimated topic traffic indicators correspond to the input topic; Determining a communication meta-score corresponding to the communication meta-score indicator based on the semantic split summary information; Determining a topic subject attitude value corresponding to the topic subject attitude indicator; The average slope of topic improvement, the topic subject word-of-mouth value, the median of topic traffic, the topic subject popularity, the total topic traffic, the estimated total topic traffic, the communication meta-score and the topic subject attitude value are determined as the topic indicator content set.

4. The method according to claim 3, wherein: Generating topic popularity information for the input topic based on the topic indicator content set includes: Adding the estimated total topic traffic of each topic in the estimated total topic traffic set to obtain a first added value; Subtracting the median of the topic traffic from the first added value to generate a first subtracted value; Multiplying the first subtraction value by the average slope of topic promotion to generate a first multiplication value; performing a negation process on the first multiplied value to generate a negated multiplied value; Using the multiplied value after negation as an exponent and the first predetermined value as a base, a first exponent value is generated; Adding the first index value to a second predetermined value to obtain a second added value; Dividing the total topic traffic by the second added value to obtain a first divided value; Dividing the communication element score by a third predetermined value to obtain a second division value; Using the second divided value as an exponent and the first predetermined value as a base, a second exponent value is generated; multiplying the second division value by the second exponent value to obtain a second multiplied value; Multiplying the topic subject popularity by the topic subject attitude value to obtain a third multiplied value; Performing absolute value processing on the topic subject's word-of-mouth value to generate an absolute word-of-mouth value; The third multiplication value is multiplied by the absolute value of the word of mouth to generate topic popularity information.

5. The method according to claim 3, wherein Determining the topic improvement average slope corresponding to the topic improvement average slope indicator according to the semantic split summary information includes: Determine, based on the semantic split summary information, the maximum value of the topic traffic, the starting value of the topic traffic, the ending value of the topic traffic, the first time point corresponding to the maximum value of the topic traffic, the second time point corresponding to the starting value of the topic traffic, and the third time point corresponding to the ending value of the topic traffic; determining a time difference between the first time point and the second time point as a first time difference; Subtract the topic traffic starting value from the topic traffic maximum value to obtain a second subtraction value; dividing the second subtracted value by the first time difference to generate a third divided value; Subtract the topic traffic end value from the topic traffic maximum value to obtain a third subtraction value; determining a time difference between the first time point and the third time point as a second time difference; Dividing the third subtraction value by the second time difference to obtain a fourth division value; Adding the third division value and the fourth division value to obtain a third added value; The third added value is divided by a third predetermined value to generate a fifth divided value as the topic improvement average slope.

6. The method according to claim 1, wherein The topic review standard files include: topic popularity review standard source file, target user review standard source file, popularity review standard source file, word-of-mouth evaluation review standard source file, word-of-mouth evaluation review standard source file, and place name popularity review standard source file; and The method of generating a topic popularity effect text in a target format corresponding to the input topic using a pre-trained large language model based on the topic popularity information and the topic comment standard file includes: Combining the content of the topic popularity evaluation standard source file, the target user evaluation standard source file, the popularity evaluation standard source file, the word-of-mouth evaluation standard source file, the word-of-mouth evaluation standard source file, and the place name popularity evaluation standard source file to generate a combined source file; The combined source file and the topic popularity information are input into the large language model to generate the topic circulation effect text.

7. The method according to claim 1, wherein The text content verification of the topic spread effect text to generate a verification result includes: For each topic review direction in the at least one topic review direction, the following third generation step is performed: Determining a first topic review content corresponding to the topic review direction using at least one pre-trained sentiment analysis neural network model; Determining a second topic review content in the topic circulation effect text that corresponds to the topic review direction; determining a content difference between the first topic review content and the second topic review content; The verification result is generated according to the obtained at least one content difference.

8. A text display device, comprising: The first acquisition unit is configured to acquire an input topic input by a target user on a target input page and a material file corresponding to the input topic, wherein the material file includes: a material file in the form of an image, a material file in the form of a video, and a material file in the form of a text; a semantic splitting unit configured to perform semantic splitting on the input topic and the material file to obtain first semantic splitting information and second semantic splitting information; an information summarizing unit, configured to summarize the first semantic splitting information and the second semantic splitting information to generate semantic splitting summary information; The determination unit is configured to determine the topic indicator content under the predetermined topic parameter indicator set based on the semantic splitting and summary information, using the available computing resources corresponding to the central processing unit and / or the available computing resources corresponding to the image processing unit, to obtain a topic indicator content set, wherein the topic indicator content set is a content set for each topic circulation platform, and the predetermined topic parameter indicator set includes: a topic promotion average slope indicator, a topic subject word-of-mouth indicator, a first topic traffic indicator, a topic subject popularity indicator, a second topic traffic indicator, an estimated topic traffic indicator, a communication meta-score indicator, and a topic subject attitude indicator, wherein determining the topic subject attitude value corresponding to the topic subject attitude indicator includes: Obtaining a topic reply information set corresponding to the input topic, wherein the topic reply information may be information replied to the input topic; Determining an image set corresponding to the topic reply information set, wherein the image set is each image that appears in the topic reply information set; For each image in the image set, the following first generation step is performed: Determining an image occurrence frequency corresponding to the image, wherein the image occurrence frequency is the number of times the image appears; Determining at least one topic reply text message corresponding to the image, wherein the at least one topic reply text message is a reply text corresponding to the image; Determining text emotion information corresponding to each topic reply text message in the at least one topic reply text message to obtain at least one text emotion information; Setting image emotional tendency information for the image based on the at least one text emotional information, wherein the number of information on each emotional tendency in the at least one text emotional information is obtained to obtain the number of each information, and selecting the emotional tendency with the highest number of corresponding information on each emotional tendency as the image emotional tendency information; Determining a topic reply text information set corresponding to the topic reply information set, wherein the topic reply information in the topic reply information set and the topic reply text information in the topic reply text information set have a one-to-one correspondence; For each topic reply information in the topic reply information set, the following second generation step is performed: Determine the topic reply text information corresponding to the topic reply information as the target topic reply text information; Determining text sentiment information corresponding to the target topic reply text information as target text sentiment information; Performing sentiment vocabulary extraction processing on the target topic reply text information to generate at least one sentiment word; Determining at least one word sentiment tendency information corresponding to the at least one sentiment word, wherein there is a one-to-one correspondence between the sentiment word in the at least one sentiment word and the word sentiment tendency information in the at least one word sentiment tendency information, the word sentiment tendency information can represent the sentiment tendency of the vocabulary, and each type of word sentiment tendency information can be set with a corresponding tendency value; Determining the image sentiment tendency information corresponding to the target topic reply information as the target image sentiment tendency information; Generate emotional tendency summary information corresponding to the topic reply information based on the target text emotional information, the at least one word emotional information and the target image emotional tendency information; Generating the topic subject attitude value based on the obtained emotional tendency summary information set, wherein each emotional tendency summary information in the emotional tendency summary information set is digitized to generate an emotional value to obtain an emotional value set, and each emotional value in the emotional value set is added to generate the topic subject attitude value; a first generating unit configured to generate topic popularity information for the input topic based on the topic indicator content set, wherein the topic indicator content in the topic indicator content set includes a topic subject attitude value; The second acquisition unit is configured to acquire a topic review standard file, wherein the topic review standard file includes: at least one standard information corresponding to at least one topic review direction; a second generating unit configured to generate, based on the topic popularity information and the topic comment standard file, a topic circulation effect text in a target format corresponding to the input topic using a pre-trained large language model, wherein the large language model corresponds to a model type selected by the target user on a topic circulation effect text generation page; a verification unit configured to perform text content verification on the topic circulation effect text to generate a verification result; The display unit is configured to, in response to determining that the verification result indicates that the text content in the topic text is correct, perform multimodal display of the topic circulation effect text on the target terminal.

9. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer-readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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