Emotion analysis method, system and equipment and storage medium

Abstract

The invention belongs to the field of sentiment analysis, and particularly relates to a sentiment analysis method, system and device and a storage medium, and the sentiment analysis method comprises the steps: responding to a starting instruction, starting to collect original state data of a user, and confirming a time window of original state data collection; performing data cleaning on the original state data to obtain cleaned data; key features are extracted based on cleaning data classification, and the extracted key features are fused into emotion features; analyzing the emotion features based on an emotion analysis model to obtain an emotion evaluation result, and feeding back the emotion evaluation result to the interaction end; and providing a corresponding activity suggestion according to an emotion evaluation result, and feeding back the activity suggestion to the interaction end.

Description

Technical Field

[0001] The present invention relates to the field of sentiment analysis, and in particular to a sentiment analysis method, system, device and storage medium. Background Art

[0002] With the development of today's society, people are under pressure from all aspects, and their psychological needs are under pressure from all aspects. They urgently need to be adjusted in time to avoid causing psychological problems and affecting their daily lives.

[0003] Before adjustment, it is necessary to confirm the current emotional state of the subject, which is usually done through questionnaire surveys or facial expression analysis of the subject to confirm the subject's emotional state for subsequent adjustment.

[0004] Current sentiment analysis uses single-modal data analysis, which has analytical limitations and inaccuracies in sentiment analysis, affecting subsequent adjustments. Summary of the Invention

[0005] In order to solve the above-mentioned problems existing in the prior art, the present invention provides a sentiment analysis method, system, device and storage medium.

[0006] A first aspect of the present invention provides a sentiment analysis method, comprising: In response to the start instruction, start collecting the user's original status data and select a time window for collecting the original status data; Performing data cleaning on the original state data to obtain cleaned data; Extracting key features based on the cleaned data classification, and fusing the extracted key features into sentiment features; Analyze the emotional characteristics based on the emotional analysis model to obtain an emotional evaluation result, and feed the emotional evaluation result back to the interactive end; According to the emotion evaluation result, corresponding activity suggestions are provided and fed back to the interactive terminal.

[0007] In one embodiment, the original status data includes voice information, video information, and motion status information.

[0008] In one embodiment, the data cleaning process includes: Eliminate environmental noise and other interference information in the original state data based on a noise filtering method; Performing difference processing on the original state data based on missing value processing to ensure the integrity of the original state data; Abnormal samples in the original state data are deleted based on abnormal data elimination.

[0009] In one embodiment, extracting key features includes: extracting speech features from the speech information; extracting video features from the video information; Extracting motion state features from the motion state information.

[0010] In one embodiment, the key feature fusion formula is:

[0011] in, It is the fusion feature; n is the number of features; is the weight of the i-th feature, and =1; is the value of the i-th feature.

[0012] In one embodiment, the response process of the start instruction includes: Provide sentiment analysis startup instructions to the interaction end; Receive the confirmation start instruction fed back by the interactive end.

[0013] In one embodiment, the step of responding to the start instruction further includes security authentication, and the security authentication includes: Provide encrypted instructions to the interactive end; Receive the decryption command fed back by the interactive end to complete the unlocking.

[0014] A second aspect of the present invention provides a sentiment analysis system, comprising: Response collection unit: used for responding to the start instruction to start collecting the user's original status data and selecting a time window for collecting the original status data; Cleaning processing unit: used for performing data cleaning processing on the original state data to obtain cleaned data; Extraction and fusion unit: used to extract key features from the cleaned data classification and fuse the extracted key features into sentiment features; Analysis and feedback unit: used to analyze the emotional features based on the emotional analysis model, obtain the emotional evaluation results, and feed back the emotional evaluation results to the interactive end; Suggestion feedback unit: used to provide corresponding activity suggestions based on the emotion evaluation results and feedback to the interaction terminal.

[0015] A third aspect of the present invention provides an electronic device, comprising: a memory for storing instructions executed by one or more processors of the electronic device, and a processor, which is one of the processors of the electronic device, used for the above-mentioned sentiment analysis method.

[0016] A fourth aspect of the present invention provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the above-mentioned sentiment analysis method.

[0017] The beneficial effects of the present invention over the prior art are: The original state data of the present invention uses multimodal data. The multimodal data collection combines three sources: the user's voice information, video information, and motion state information. This multimodal fusion method can fully explore the complex characteristics of the user's emotional expression and overcome the information loss problem that may be caused by single-modal analysis; the data cleaning technology further eliminates the interference caused by noise, missing and abnormal data, greatly improving the reliability of the analysis results; the time window setting function allows users to flexibly select the time period that needs to be paid attention to and adjust it according to specific activities or focus requirements; users do not need to actively organize or upload data. The present invention automatically completes the data collection, processing and analysis process after responding to the start instruction, and finally feeds back the emotional evaluation results to the interactive end; provides practical and feasible activity suggestions for different emotional states, which helps users relieve negative emotions or maintain a positive attitude and improve their quality of life. For example, "jogging" can relieve anxiety, "interacting with friends" can maintain a high mood, and personalized suggestions enhance user happiness. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 According to an embodiment of the present invention, a flow chart of a sentiment analysis method is shown.

[0020] Figure 2 According to an embodiment of the present invention, a flow chart of a response process of a start instruction is shown.

[0021] Figure 3 According to an embodiment of the present invention, a security authentication flow chart is shown.

[0022] Figure 4 According to an embodiment of the present invention, a structural diagram of a sentiment analysis system is shown.

[0023] Figure 5 According to an embodiment of the present invention, a schematic structural diagram of an electronic device is shown.

[0024] Figure 6According to an embodiment of the present invention, a schematic structural diagram of a computer-readable storage medium is shown. DETAILED DESCRIPTION

[0025] The following describes the embodiments of the present invention through specific examples. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied in various other specific embodiments, and the various details of the present invention may be modified or altered based on different viewpoints and application systems without departing from the spirit of the present invention. It should be noted that the embodiments and features of the embodiments of the present invention may be combined with each other unless they conflict.

[0026] The following is a detailed description of the embodiments of the present invention with reference to the accompanying drawings so that those skilled in the art can easily implement the present invention. The present invention can be embodied in many different forms and is not limited to the embodiments described herein.

[0027] In the description of the present invention, reference to the terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and integrate different embodiments or examples described in the present invention, as well as features of different embodiments or examples, unless otherwise inconsistent.

[0028] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the context of this invention, "plurality" means two or more, unless otherwise specifically defined.

[0029] In order to clearly describe the present invention, components not related to the description are omitted, and the same or similar components are denoted by the same reference numerals throughout the specification.

[0030] Throughout this specification, when a device is said to be "connected" to another device, this includes not only "direct connection" but also "indirect connection" with other elements interposed therebetween. Furthermore, when a device is said to "include" a certain component, unless otherwise stated, this does not exclude the inclusion of other components but rather implies that the device may include other components.

[0031] When a device is said to be "on" another device, it may be directly on the other device, but there may also be other devices between it. In contrast, when a device is said to be "directly on" another device, there are no other devices between it.

[0032] Although the terms first, second, etc. are used in some instances herein to represent various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, the first interface and the second interface, etc. are represented. Furthermore, as used in this article, the singular forms "one," "an," and "the" are intended to also include the plural forms, unless there is a contrary indication in the context. It should be further understood that the terms "comprise," "include," and "include" indicate the presence of features, steps, operations, elements, components, items, types, and / or groups, but do not exclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, types, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or mean any one or any combination. Therefore, "A, B, or C" or "A, B, and / or C" means "any of the following: A; B; C; A and B; A and C; B and C; A, B, and C." Exceptions to this definition only occur when the combination of elements, functions, steps, or operations is inherently mutually exclusive in some way.

[0033] The technical terms used herein are intended only to refer to specific embodiments and are not intended to limit the present invention. The singular as used herein also includes the plural unless expressly stated to the contrary. The term "comprising" as used in this specification specifies specific features, regions, integers, steps, operations, elements, and / or components and does not exclude the presence or addition of other features, regions, integers, steps, operations, elements, and / or components.

[0034] Although not defined differently, all terms used herein, including technical and scientific terms, have the same meanings as those generally understood by those skilled in the art to which this invention belongs. Terms defined in commonly used dictionaries are to be interpreted as having meanings consistent with the relevant technical literature and current knowledge, and unless otherwise defined, they should not be overly interpreted as ideal or highly formalized meanings.

[0035] The following problems exist in the existing technology: the current sentiment analysis adopts single-modal data analysis, which has analysis limitations and inaccuracies in sentiment analysis, affecting subsequent adjustments.

[0036] The sentiment analysis method proposed in this invention uses multimodal data. This multimodal fusion method can fully explore the complex characteristics of user emotional expression and overcome the information loss problem that may be caused by single-modal analysis; data cleaning technology further eliminates the interference caused by noise, missing and abnormal data, greatly improving the reliability of the analysis results, ensuring the accuracy of subsequent adjustments, and improving the actual experience.

[0037] In some embodiments of the present invention, Figure 1 As shown, a sentiment analysis method includes: Step 110: Respond to the start instruction to start collecting the user's original state data and select a time window for collecting the original state data; wherein the original state data includes voice information, video information and motion state information.

[0038] Step 120: clean the original state data to obtain cleaned data. It can be understood that data cleaning is used to process the original state data to eliminate the impact of environmental noise, missing data and abnormal data on subsequent analysis.

[0039] Step 130: Extract key features based on the cleaned data classification, and fuse the extracted key features into sentiment features; it can be understood that the cleaned data is a collection of data of different categories, and the key features in each category of data are extracted separately, and the key features of all categories are fused into sentiment features for subsequent sentiment analysis of users.

[0040] Step 140: Analyze the emotional features based on the sentiment analysis model to obtain a sentiment evaluation result, which is then fed back to the interactive terminal. The sentiment evaluation results include positive, neutral, and negative sentiments. The interactive terminal is a display. It is understood that the emotional features are input into the sentiment analysis model, which then feeds the sentiment evaluation results back to the interactive terminal for the user to view.

[0041] Step 150: Based on the emotion evaluation results, corresponding activity suggestions are provided and fed back to the interactive terminal. It is understood that corresponding activity suggestions are provided for different emotion evaluation results, and the user can adjust the user's current emotional state based on the activity suggestions on the interactive terminal. The following will further explain the specific implementation of the above steps 110 to 150: In the above embodiment, in step 110, voice information is acquired by a voice acquisition device, video information is acquired by an image acquisition device, and motion status information is calculated using data acquired by an accelerometer. In this embodiment, the time window is one minute. Collecting the user's raw status data also includes displaying a time window input box on the display, allowing the user to enter the time window, providing flexibility.

[0042] In the above embodiment, in the aforementioned step 120, data cleaning includes removing environmental noise and other interference information in the original state data based on a noise filtering method; performing difference processing on the original state data based on missing value processing to ensure the integrity of the original state data; and deleting abnormal samples in the original state data based on abnormal data elimination.

[0043] In the above embodiment, in the aforementioned step 130, extracting key features includes: extracting voice features from voice information; extracting video features from video information; extracting motion state features from motion state information; wherein, voice features include pitch, volume, speaking speed and vocabulary content, etc.; video features include facial expressions and gaze direction, etc.; motion state features include movement rate, movement amplitude and gait pattern; specific example: vocabulary content includes emotion description-related words such as happy and frustrated; facial expressions include calm, smiling, laughing, etc.; gait patterns include stillness, walking, and running.

[0044] Key feature fusion formula:

[0045] in, It is the fusion feature; n is the number of features; is the weight of the i-th feature, and =1; is the value of the i-th feature.

[0046] For example, the characteristics of the tone in the speech feature include: low, medium, and high, and their corresponding characteristic values ​​( ) is 1, 2, 3. The facial expression features in the video features include: calm, smiling, laughing, and their corresponding feature values ​​( ) is 1, 2, 3; the gait mode features in the motion state features include: still, walking, running, and their corresponding feature values ​​( ) is 1, 2, 3. Tone weight =0.4, facial expression weight =0.3, gait pattern weight =0.3. It should be noted that the voice features, video features and motion state features are only some examples for illustration, and thus the key features of the pendant of the present invention are not limited to the above-mentioned ones.

[0047] Collect the user's original state data, which includes a piece of voice information, video information and motion state data. Clean the original state data and classify and extract the voice features, video features and motion state features in the cleaned data. Among them, the tone is medium, and its corresponding feature value is =2, the facial expression is smiling, and its corresponding eigenvalue =2, the gait mode is walking, and its corresponding eigenvalue =2, according to the key feature fusion formula we can get: = × + × + ×

[0048] Substituting specific values ​​into the equation, we get: =0.4×2+0.3×2+0.3×2=2.0 The above key feature fusion value =2.0 is substituted into the sentiment analysis model for sentiment analysis.

[0049] In the above embodiment, in steps 140 and 150, the emotion assessment results and activity suggestions are displayed on the display via text, and can also be delivered via voice. Specifically, the emotion assessment results and activity suggestions are displayed to the user simultaneously with the text display. Alternatively, a selection box for voice display can be provided on the display, and the user can click the selection box on the display to confirm whether voice display is still required. This is not limited here.

[0050] Through the sentiment analysis method adopted in the above steps 110 to 150, the user sends a confirmation start instruction through the interactive terminal to enter the sentiment analysis mode. The user sets a time window on the interactive terminal, for example, one minute, and collects the user's original state data within the time window. The original state data is then cleaned to obtain cleaned data, and the cleaned data is classified to extract key features, and the key features are fused to obtain sentiment features. The sentiment features are input into the sentiment analysis system for analysis to obtain sentiment evaluation results, and the sentiment evaluation results are fed back to the interactive terminal. At the same time, activity suggestions are provided based on the sentiment evaluation results and fed back to the interactive terminal. The original state data of the present invention adopts multimodal data. The multimodal data collection combines three sources of user voice information, video information and motion state information. This multimodal fusion method can fully explore the complex characteristics of user emotional expression and overcome the information loss problem that may be caused by single-modal analysis; data cleaning technology further eliminates the interference caused by noise, missing and abnormal data, greatly improving the reliability of the analysis results; the time window setting function allows users to flexibly select the time period that needs to be paid attention to and adjust it according to specific activities or focus requirements; users do not need to actively organize or upload data. The present invention automatically completes the data collection, processing and analysis process after responding to the start instruction, and finally feeds back the emotional evaluation results to the interactive end; provides practical and feasible activity suggestions for different emotional states, which helps users relieve negative emotions or maintain a positive attitude and improve the quality of life. For example, "jogging" can relieve anxiety and "interacting with friends" can maintain high emotions. Personalized suggestions enhance user happiness.

[0051] In some embodiments of the present disclosure, the sentiment analysis model in step 140 includes using a convolutional neural network (CNN) to process video feature data, a recurrent neural network (RNN) and its variants to process speech feature data for analysis, and a random forest or k-nearest neighbor (KNN) machine learning algorithm to analyze motion feature data. Finally, the outputs of the different models are fused to produce a sentiment assessment result. In this embodiment, the outputs of the different models are fused using category labels to produce a sentiment assessment result. For example, if the CNN model outputs an assessment result of "positive," the RNN model outputs an assessment result of "positive," and the random forest model outputs an assessment result of "neutral," the fusion result indicates that since "positive" appears the most frequently, the final category is "positive." It is worth mentioning that the output results of different models can also be fused using the weighted decision method or the probability distribution method to output the sentiment evaluation results; among them, the weighted decision method: assign weights to each model according to the importance of each model to the sentiment classification results (algorithm performance). For example, CNN performs best in facial expression classification and is assigned a weight of 0.5, while RNN or random forest have slightly lower weights, each assigned 0.25. These weights are combined to determine the final category; the probability distribution method: if each model outputs not only the category label but also the probability distribution of the sentiment category (for example: positive = 60%, negative = 30%, neutral = 10%), the probability distribution of each model can be fused by weighted averaging to obtain the final category probability (such as positive = 65%, negative = 25%, neutral = 10%), and the category with the highest probability is selected as the final label.

[0052] In some embodiments of the present disclosure, Figure 2 A flowchart showing a response process of a start instruction in the aforementioned step 110 is shown. Figure 2 As shown in the figure, the response process of the start command includes: Step 111: providing a start instruction for sentiment analysis to the interactive end; specifically, the start instruction is a start button provided on the display for the user to click, and when the user clicks the start button, it indicates that the user confirms the start instruction at the interactive end.

[0053] Step 112: Receive the confirmation start instruction fed back by the interaction terminal. It can be understood that, upon receiving the confirmation start instruction from the user at the interaction terminal, the user's original status data is collected.

[0054] The response start instruction adopted in the above steps 111 to 112 can be started according to the needs of the user. In actual application, when the user needs to perform emotional state analysis, the system will start to collect and process the original state data after sending a confirmation start instruction through the interactive terminal, thereby improving the autonomy and controllability of the operation; the collection process of the original state data is based on the user's explicit authorization, and data leakage without the user's consent can be avoided from startup to data collection, thereby ensuring privacy protection.

[0055] In some embodiments of the present disclosure, before responding to the start instruction in the aforementioned step 110, a security authentication is also included. Figure 3 A safety authentication flow chart is shown, such as Figure 3 As shown, security certification includes: Step 101: providing an encryption instruction to the interactive end; specifically, the encryption instruction includes password confidentiality or graphic encryption; it is understandable that enabling the sentiment analysis of the present invention requires security authentication to ensure the privacy of the user.

[0056] Step 102: Receive the decryption instruction fed back by the interactive terminal and complete the unlocking. It is understandable that the user can continue with subsequent operations only after entering the correct numeric password or graphic password.

[0057] Through the above steps 101 to 102, security authentication is adopted. Security authentication is required when the user performs sentiment analysis to ensure the privacy security of the user's sentiment analysis data.

[0058] In some embodiments of the present disclosure, the activity recommendations in step 150 include providing emotion regulation recommendations, lifestyle recommendations, personalized learning resource recommendations, social activity recommendations, and entertainment content recommendations based on the emotion assessment results. Emotion regulation recommendations include: deep breathing, meditation, or relaxing; lifestyle recommendations include: diet, exercise, and sleep-related; personalized learning resource recommendations include: learning materials, courses, and educational resources; social activity recommendations include location-based push notifications of attractions, community activities, or catering services suitable for the user's emotional state; and entertainment content recommendations include push notifications of resources such as movies, TV shows, games, or books. The rich activity recommendations provided by the present invention based on the emotion assessment results can be used to regulate the user's emotional state or maintain a certain emotional state.

[0059] In some embodiments of the present disclosure, the sentiment analysis system in the sentiment analysis method provided by the present invention further includes optimizing the sentiment analysis model. The optimized sentiment analysis model is optimized based on user feedback and usage data to improve the accuracy and efficiency of the analysis. Specifically, optimizing the sentiment analysis model includes: Continuously optimize deep learning network structure and parameter configuration to improve the accuracy of sentiment analysis; Dynamically adjust the input weights of different functional modules to meet the needs of different usage scenarios; Introduce new sensor devices and efficient data processing technologies to expand application scenarios and enhance user experience.

[0060] When the sentiment analysis method of the present application is applied on a mobile phone, the security authentication can be the system password or graphic protection provided by the mobile phone, or the secondary protection after the mobile phone is unlocked; the response start command is the response voice information to the user clicking to enter the sentiment analysis operation (collected through the mobile phone's built-in microphone), video information is collected through the mobile phone's built-in camera, and motion status information is collected through the mobile phone's built-in accelerometer (obtained through calculation or captured by the motion software in the mobile phone); data cleaning and key feature extraction are processed by calling the mobile phone's CPU, GPU, and NPU or uploading the data to the cloud for cloud processing; the sentiment analysis model is processed by calling the mobile phone's CPU, GPU, and NPU or uploading the data to the cloud for cloud processing; the sentiment evaluation results are fed back to the mobile phone's display; activity suggestions include the above-mentioned practical and feasible activity suggestions for different emotional states, and can also be combined with calling the map positioning of the corresponding location using the map software in the mobile phone to push nearby attractions, communities, restaurants, etc.; entertainment content, such as accessing relevant third-party interfaces to push movies, TV programs, games, or books, enriches the content of activity suggestions and their fit with actual usage scenarios, thereby enhancing the actual experience.

[0061] In specific applications, the sentiment analysis method provided by this invention can be widely used in scenarios such as health management, psychological counseling, and user experience optimization. For example, in mental health applications, it can help users monitor their own emotions; in social media recommendation algorithms, it can push content that suits users' current moods.

[0062] In some embodiments of the present disclosure, Figure 4 A structural diagram of a sentiment analysis system is provided. Figure 4 As shown, this sentiment analysis system is used to implement the sentiment analysis method provided in the above embodiment, and may specifically include: Response collection unit 501: used to respond to the start instruction to start collecting the user's original status data and select a time window for collecting the original status data; Cleaning processing unit 502: used to perform data cleaning processing on the original state data to obtain cleaned data; Extraction and fusion unit 503: used to extract key features from cleaned data classification and fuse the key features of all categories into sentiment features; Analysis and feedback unit 504: used to analyze the emotional features based on the emotional analysis model, obtain the emotional evaluation results, and feed back the emotional evaluation results to the interactive terminal; Suggestion feedback unit 505: used to provide corresponding activity suggestions based on the emotion evaluation results and feedback to the interactive terminal.

[0063] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods, or program products. Therefore, various aspects of the present disclosure may be implemented in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits," "modules," or "platforms."

[0064] Specifically, Figure 5 According to an embodiment of the present disclosure, a schematic diagram of the structure of an electronic device is shown. Figure 5 hereinafter, an electronic device 600 according to this embodiment of the present disclosure is described. Figure 5 The electronic device 600 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0065] like Figure 5 As shown, electronic device 600 is implemented as a general-purpose computing device. Components of electronic device 600 may include, but are not limited to, at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), and a display unit 640.

[0066] The storage unit stores program codes, which can be executed by the processing unit 610 so that the processing unit 610 performs the steps according to various exemplary embodiments of the present disclosure. For example, the processing unit 610 can perform the following steps: Figure 1 The relevant steps of the sentiment analysis method shown in .

[0067] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0068] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0069] Bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0070] The electronic device 600 can also communicate with one or more external devices 700 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and / or any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication can occur via an input / output (I / O) interface 650. Furthermore, the electronic device 600 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 660. The network adapter 660 can communicate with other modules of the electronic device 600 via the bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the electronic device 600, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0071] The present disclosure also provides a computer-readable storage medium for storing a program that, when executed, implements the steps of the sentiment analysis method. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product, which includes program code. When the program product is executed on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the above-mentioned copywriting generation method section of this specification.

[0072] Specifically, Figure 6 According to an embodiment of the present disclosure, a schematic diagram of the structure of a computer-readable storage medium is shown. Figure 6 As shown, a program product 800 for implementing the above-mentioned sentiment analysis method according to an embodiment of the present disclosure is described. The program product 800 may be a portable compact disc read-only memory (CD-ROM) and includes program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, a 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, system, or device.

[0073] The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable 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 thereof.

[0074] Computer-readable storage media may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, system, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0075] The program code for performing the specific implementation operations of the sentiment analysis method provided by the aforementioned embodiments of the present disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0076] In summary, through the technical solution provided by the present disclosure, the proposed sentiment analysis method adopts multimodal data. The multimodal data collection combines three sources: the user's voice information, video information, and motion status information. This multimodal fusion method can fully explore the complex characteristics of the user's emotional expression and overcome the information loss problem that may be caused by single-modal analysis; the data cleaning technology further eliminates the interference caused by noise, missing and abnormal data, greatly improving the reliability of the analysis results; the time window setting function allows users to flexibly select the time period that needs to be paid attention to and adjust it according to specific activities or focus requirements; users do not need to actively organize or upload data. The present invention automatically completes the data collection, processing and analysis process after responding to the start instruction, and finally feeds back the emotional evaluation results to the interactive end; provides practical and feasible activity suggestions for different emotional states, which helps users relieve negative emotions or maintain a positive attitude and improve the quality of life. For example, "jogging" can relieve anxiety, "interacting with friends" can maintain a high mood, and personalized suggestions enhance user happiness.

[0077] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this invention are intended to be covered by the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A sentiment analysis method, characterized in that: include: In response to the start instruction, start collecting the user's original status data and select a time window for collecting the original status data; Performing data cleaning on the original state data to obtain cleaned data; Extracting key features based on the cleaned data classification, and fusing the extracted key features into sentiment features; Analyze the emotional characteristics based on the emotional analysis model to obtain an emotional evaluation result, and feed the emotional evaluation result back to the interactive end; According to the emotion evaluation result, corresponding activity suggestions are provided and fed back to the interactive terminal.

2. The sentiment analysis method according to claim 1, wherein: The original state data includes voice information, video information and motion state information.

3. The sentiment analysis method according to claim 2, wherein: Data cleaning processing includes: Eliminate environmental noise and other interference information in the original state data based on a noise filtering method; Performing difference processing on the original state data based on missing value processing to ensure the integrity of the original state data; Abnormal samples in the original state data are deleted based on abnormal data elimination.

4. The sentiment analysis method according to claim 2, wherein: The key feature extraction includes: Extracting voice features from the voice information; extracting video features from the video information; Extracting motion state features from the motion state information.

5. The sentiment analysis method according to claim 1, wherein: The key feature fusion formula is: in, It is the fusion feature; n is the number of features; is the weight of the i-th feature, and =1; is the value of the i-th feature.

6. The sentiment analysis method according to claim 1, wherein: The response process of the start instruction includes: providing a start instruction of sentiment analysis to the interaction terminal; Receive the confirmation start instruction fed back by the interactive end.

7. The sentiment analysis method according to claim 6, wherein: The response start instruction also includes a security authentication, and the security authentication includes: Provide encrypted instructions to the interactive end; Receive the decryption command fed back by the interactive end to complete the unlocking.

8. A sentiment analysis system, characterized in that: include: Response collection unit: used for responding to the start instruction to start collecting the user's original status data and selecting a time window for collecting the original status data; Cleaning processing unit: used for performing data cleaning processing on the original state data to obtain cleaned data; Extraction and fusion unit: used to extract key features of the cleaned data classification and fuse the key features of all categories into sentiment features; Analysis and feedback unit: used to analyze the emotional features based on the emotional analysis model, obtain the emotional evaluation results, and feed back the emotional evaluation results to the interactive end; Suggestion feedback unit: used to provide corresponding activity suggestions based on the emotion evaluation results and feedback to the interaction terminal.

9. An electronic device, characterized in that: include: A memory for storing instructions executed by one or more processors of an electronic device, and a processor, which is one of the processors of the electronic device, for use in the sentiment analysis method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the sentiment analysis method according to any one of claims 1 to 7.

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