A method and system for determining the frequency of dramatic conflict based on a script

Abstract

The present application discloses a method and system for determining the frequency of dramatic conflict based on a script, the method comprising: obtaining a text paragraph from a script, wherein the text paragraph describes at least one character and the performance information corresponding to the character; determining the emotion of the character based on the performance information corresponding to the character; obtaining a timeline corresponding to the performance information, and arranging the emotions of the character on a two-dimensional coordinate system according to the chronological order on the timeline; displaying the two-dimensional coordinate system, wherein the displayed two-dimensional coordinate system is used as a shooting outline, and the emotions on the two-dimensional coordinate system serve as the basis for determining the dramatic conflict in the scene. This application solves the problem of subjective script evaluation caused by the inability to objectively display the conflicts in the script in the related art, thereby being able to objectively display the emotional changes in the script, providing objective data for evaluating the prominence in the script.

Description

Technical Field

[0001] The present application relates to the field of software, and in particular to a method and system for determining the frequency of dramatic conflict based on a script. Background Art

[0002] A script, primarily composed of dialogue and stage directions, serves as the foundational text for theatrical creation and the basis for director and actor performance. Similar terms include script, play, and playwright. It is a literary form that primarily uses a dialogic format to present a storyline. Scripts are primarily categorized as literary and cinematographic. Literary scripts emphasize literary qualities and tend to be less cinematographic, and include drama scripts (or play scripts), novel scripts (or play novels), sketch scripts, and crosstalk scripts. Cinetographic scripts emphasize filming, and their literary and artistic qualities can be high or low (depending on the film's subject matter, market, and funding). These scripts include scene scripts, storyboards (or film scripts), and scripts. A script is an essential tool for stage performance or filming, serving as the language of dialogue between characters. It is an art form dedicated to stage performance, distinct from drama and other literary genres.

[0003] The quality of a film largely depends on its compelling story, which requires sufficient conflict in the script. Currently, evaluating the amount of conflict in a script relies primarily on subjective readings, which relies on human factors and makes it impossible to objectively measure the script. Consequently, no solution exists in the relevant technology that can objectively display the amount of conflict. Summary of the Invention

[0004] The embodiments of the present application provide a method and system for determining the frequency of dramatic conflicts based on a script, so as to at least solve the problem of subjective script evaluation caused by the inability to objectively display the conflicts in the script in the related art.

[0005] According to one aspect of the present application, a method for determining the frequency of dramatic conflict based on a script is provided, which is applied to software, and the software is used to execute the method, and the method includes the following steps: obtaining a text paragraph from a script, wherein the text paragraph is a text paragraph under the same shooting scene, and the text paragraph describes at least one character and performance information corresponding to the character, wherein the performance information includes at least one of the following: language, action, and expression; determining the emotion of a character based on the performance information corresponding to the character, wherein the emotion is determined based on the text description in the performance information, and the emotion is a preset emotion; obtaining a timeline corresponding to the performance information, and arranging the emotions of the character on a two-dimensional coordinate system according to the time sequence on the timeline; wherein the horizontal axis of the two-dimensional coordinate system is the timeline, and the vertical axis of the two-dimensional coordinate system is the emotion, and different emotions have different positions on the vertical axis; displaying the two-dimensional coordinate system, wherein the displayed two-dimensional coordinate system is used as a shooting outline, and the emotions on the two-dimensional coordinate system serve as the basis for determining the dramatic conflict in the scene.

[0006] Furthermore, obtaining the text paragraphs from the script includes: dividing the script into different chapters according to the text in the script used to identify chapters; obtaining the corresponding scenes in each chapter, wherein the scenes are shooting scenes; and using the text in the same scene as the text paragraphs.

[0007] Furthermore, it also includes: generating a corresponding two-dimensional coordinate system for each scene in all chapters of the script; arranging all the generated two-dimensional coordinate systems in chronological order according to the scenes corresponding to the two-dimensional coordinate systems in the chapter; displaying the arranged two-dimensional coordinate systems in the chapter; arranging all chapters in chronological order, and displaying the two-dimensional coordinate systems corresponding to all the arranged chapters.

[0008] Furthermore, determining the emotions of a character based on the performance information corresponding to the character includes: obtaining words in the performance information used to describe the character's actions and / or expressions, or obtaining the language of the character in the performance information, and obtaining words from the language used to indicate the character's emotions; dividing the words into positive words, neutral words and negative words, wherein the positive words correspond to a first emotion, the neutral words correspond to a second word, and the negative words correspond to a third emotion, and the first emotion, the second emotion and the third emotion are different emotions; and determining the emotions of the character based on the words.

[0009] According to another aspect of the present application, a system for determining the frequency of dramatic conflict based on a script is provided, which is applied to software, and the software includes the following modules: an acquisition module for acquiring a text paragraph from a script, wherein the text paragraph is a text paragraph under the same shooting scene, and the text paragraph describes at least one character and the performance information corresponding to the character, wherein the performance information includes at least one of the following: language, action, and expression; a determination module for determining the emotion of a character based on the performance information corresponding to the character, wherein the emotion is determined based on the text description in the performance information, and the emotion is several preset emotions; an arrangement module for acquiring the time axis corresponding to the performance information, and arranging the emotions of the character on a two-dimensional coordinate system according to the time sequence on the time axis; wherein the horizontal axis of the two-dimensional coordinate system is the time axis, and the vertical axis of the two-dimensional coordinate system is the emotion, and different emotions have different positions on the vertical axis; a display module for displaying the two-dimensional coordinate system, wherein the displayed two-dimensional coordinate system is used as a shooting outline.

[0010] Furthermore, the acquisition module is used to: divide the script into different chapters according to the text used to identify chapters in the script; obtain the corresponding scene in each chapter, wherein the scene is a shooting scene; and use the text in the same scene as the text paragraph.

[0011] Furthermore, the display module is also used to: generate a corresponding two-dimensional coordinate system for each scene in all chapters of the script; arrange all the generated two-dimensional coordinate systems in chronological order according to the scenes corresponding to the two-dimensional coordinate systems in the chapter; display the arranged two-dimensional coordinate systems in the chapter; arrange all chapters in chronological order, and display the two-dimensional coordinate systems corresponding to all the arranged chapters.

[0012] Furthermore, the determination module is used to: obtain words from the performance information used to describe the character's actions and / or expressions, or obtain the language of the character in the performance information, and obtain words from the language used to indicate the character's emotions; divide the words into positive words, neutral words, and negative words, wherein the positive words correspond to a first emotion, the neutral words correspond to a second word, and the negative words correspond to a third emotion, and the first emotion, the second emotion, and the third emotion are different emotions; determine the character's emotions based on the words.

[0013] According to another aspect of the present application, an electronic device is also provided, comprising a memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the above-mentioned method steps.

[0014] According to another aspect of the present application, a readable storage medium is provided, on which computer instructions are stored, wherein the computer instructions implement the above method steps when executed by a processor.

[0015] In an embodiment of the present application, a text paragraph is obtained from a script, wherein the text paragraph is a text paragraph under the same shooting scene, and the text paragraph describes at least one character and the performance information corresponding to the character, wherein the performance information includes at least one of the following: language, action, expression; the emotion of the character is determined according to the performance information corresponding to a character, wherein the emotion is determined according to the text description in the performance information, and the emotion is a preset emotion; the time axis corresponding to the performance information is obtained, and the emotions of the character are arranged on a two-dimensional coordinate system according to the time order on the time axis; wherein the horizontal axis of the two-dimensional coordinate system is the time axis, and the vertical axis of the two-dimensional coordinate system is the emotion, and different emotions have different positions on the vertical axis; the two-dimensional coordinate system is displayed, wherein the displayed two-dimensional coordinate system is used as a shooting outline, and the emotions on the two-dimensional coordinate system are used as the basis for determining the dramatic conflict in the scene. This application solves the problem of subjective script evaluation caused by the inability to objectively display the conflict in the script in the related art, thereby being able to objectively display the emotional changes in the script, providing objective data for evaluating the prominence in the script. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0017] Figure 1 is a flow chart of a method for determining the frequency of dramatic conflict based on a script according to an embodiment of the present application;

[0018] Figure 2 is a schematic diagram of two-dimensional coordinates of emotional points according to an embodiment of the present application; and

[0019] Figure 3 It is a schematic diagram of the two-dimensional coordinates of the emotional points of character A related to the event according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0021] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0022] Scripts, especially those used for filming TV dramas, need to be presented to the audience in the form of images. The conflicts felt by the audience are basically expressed through the actors' performances of their roles. Therefore, the following implementation methods objectively present the emotional changes of the characters in the script. This allows film and television workers to clearly and objectively evaluate whether the conflict in a scene in a script is sufficient, providing an objective basis for script modification and even future filming.

[0023] In the following embodiment, a method for determining the frequency of dramatic conflict based on a script is provided, which is applied to software, and the software is used to execute the method. Figure 1 is a flow chart of a method for determining the frequency of dramatic conflict based on a script according to an embodiment of the present application. Figure 1 As shown, the steps included in the method are described below.

[0024] Step S102, obtaining a text paragraph from the script, wherein the text paragraph is a text paragraph under the same shooting scene, and the text paragraph describes at least one character and the performance information corresponding to the character, wherein the performance information includes at least one of the following: language, action, and expression.

[0025] Step S104 , determining the emotion of a character according to the performance information corresponding to the character, wherein the emotion is determined according to the text description in the performance information, and the emotion is one of several preset emotions.

[0026] Step S106, obtain the time axis corresponding to the performance information, and arrange the emotions of the character on a two-dimensional coordinate system according to the time sequence on the time axis; wherein the horizontal axis of the two-dimensional coordinate system is the time axis, and the vertical axis of the two-dimensional coordinate system is the emotion, and different emotions have different positions on the vertical axis.

[0027] Step S108, displaying the two-dimensional coordinate system, wherein the displayed two-dimensional coordinate system is used as a shooting outline, and the emotions on the two-dimensional coordinate system serve as the basis for determining the dramatic conflict in the scene, and each emotion in the two-dimensional coordinate system is mapped to a value on the vertical axis. The closer the position on the vertical axis is to the origin of the coordinate system, the lower the emotion corresponding to the value on the vertical axis, and the farther away from the origin of the coordinate system, the higher the emotion corresponding to the value on the vertical axis. Each emotion corresponds to a point on the two-dimensional coordinate system.

[0028] In an additional embodiment, points corresponding to emotions displayed in a two-dimensional coordinate system can be connected to form a curve, which can be called an emotion curve. This curve is compared with a pre-configured curve. If the similarity between the curve and the pre-configured curve exceeds a threshold, the scene corresponding to the curve is determined to meet the requirements. The pre-configured curve can be generated based on the emotions in a scene in a script selected by experts; the scripts selected by the experts can be classic and excellent scripts.

[0029] Through the above steps, the problem of subjective script evaluation caused by the inability to objectively display the conflicts in the script in related technologies is solved, so that the emotional changes in the script can be objectively displayed, providing objective data for evaluating the prominence in the script.

[0030] Usually a script may also include different chapters. In order to distinguish chapters, in an optional embodiment, obtaining the text paragraphs from the script may include the following steps: dividing the script into different chapters according to the text in the script used to identify chapters; for each chapter, obtaining the corresponding scene in the chapter, wherein the scene is a shooting scene; and using the text in the same scene as the text paragraph.

[0031] Corresponding to each chapter, in an optional embodiment, a corresponding two-dimensional coordinate system is generated for each scene in all chapters of the script; all generated two-dimensional coordinate systems are arranged in chronological order according to the scenes corresponding to the two-dimensional coordinate systems in the chapter; the arranged two-dimensional coordinate systems are displayed in the chapter; all chapters are arranged in chronological order, and the two-dimensional coordinate systems corresponding to all arranged chapters are displayed.

[0032] There are many ways to determine emotions. For example, the following method for extracting emotions from microblogs can also be applied to this embodiment. The following method is described in detail.

[0033] Step 1: Collect original Weibo comment text data for training the capsule network, and preprocess the original Weibo comment text data to obtain a Weibo text dataset, which includes a training set and a test set.

[0034] Step 2: Pre-train the Weibo texts in the Weibo text dataset and convert the Weibo texts in the Weibo text dataset into word2vec feature word vectors.

[0035] Step 3: Using the emotion dictionary, select sentences containing emotions from the pre-trained microblog texts to be classified in the training set as local emotion features.

[0036] Step 4: Use the capsule network trained with the training set and test set to extract the overall emotional features of the microblog text to be classified.

[0037] Step 5: Fusing the overall emotion features and local emotion features of the microblog texts to be classified in the microblog text dataset to obtain emotion classification results of the microblog texts to be classified, wherein the emotion classification results include positive emotion probability and negative emotion probability.

[0038] Step 6: Sum and average the emotion classification results of all the to-be-classified microblog texts of a microblog user in the microblog text data set, and determine the emotional tendency of the microblog user based on the average value.

[0039] Specifically, in this embodiment, the Weibo comment sentiment classification method utilizes local and global sentiment features and uses a trained capsule network to classify Weibo comments. The detailed steps are as follows. In step 1, raw Weibo comment text data is collected for training the capsule network model parameters. This raw Weibo comment text data is then normalized and preprocessed to generate a preprocessed Weibo text dataset. This Weibo text dataset is then divided into a training set and a test set.

[0040] The raw Weibo comment data from a specific Weibo user's Weibo platform is crawled and preprocessed. Specifically, a web crawler is used to collect the raw Weibo comment text data and filter the data to remove comments containing images, special symbols, and emoticons. Then, the filtered comment data is manually annotated with sentiment to obtain corresponding Weibo text labels. Sentiments are categorized into two types: positive and negative. For example, sentences with positive sentiment, such as "Updated to Weibo, posted photos, so handsome!", are labeled as 1; sentences with negative sentiment, such as "I'm so sleepy now, can I do anything else?", are labeled as 0. This serves as the Weibo text dataset. Finally, the preprocessed Weibo text dataset is divided into a training dataset and a test dataset in a certain ratio, for example, a 4:1 ratio. In step 2, individual Weibo texts in the training dataset are segmented using Chinese punctuation marks. Then, a sentiment dictionary is used to select sentences containing sentiment, which are used as local sentiment features. In this embodiment, a sentiment dictionary is used to mark sentences with emotional tendencies so that they can be automatically recognized by the capsule network model. Sentences are segmented by punctuation, such as commas, periods, semicolons, exclamation points, and question marks. In the capsule network model, sentences represent local content within a Weibo text. The capsule network model can learn the emotional characteristics of these local content and obtain the corresponding sentiment classification probabilities. If a Weibo text can be extracted into k sentences, i.e., k parts, the Weibo text is input as a whole during training. The capsule network model automatically identifies sentences with emotional tendencies using the sentiment dictionary and learns their sentiment classification probabilities. The top m probabilities are then selected based on their magnitude and retained for calculation of the final sentiment classification prediction results. In step three, a vocabulary is constructed and the Weibo text dataset is pre-trained to obtain the corresponding Word2vec feature word vectors. In this embodiment, a Chinese word segmentation tool, such as the word segmentation tool Jieba, is used to segment the Weibo text, and the word2vec tool is trained on the Weibo text dataset to convert the sentences into corresponding word vector matrices. For example, if a microblog text s consists of n words, after word segmentation, it can be represented as: s = (w1, w2, w3, ... wn), and each word w is represented as a k-dimensional real vector. Ultimately, each sentence is represented as an n × k matrix. In step 4, the overall sentiment features of the microblog text to be classified are extracted: a capsule network (CapsuleNetwok) is used to extract the overall sentiment features of the microblog text.In this embodiment, a single Weibo text is used as an input and input into the capsule network for learning, and a binary classification result represented by probability is obtained. The designed deep learning model can also learn the characteristics of sentences containing emotions. The output result is the same as the Weibo text form, and the output form is {y1, y2}, where y1 is positive emotion and y2 is negative emotion.

[0041] The specific method for extracting the overall emotional features of Weibo text includes the following steps: A: Constructing a capsule network. In order to extract the local and overall emotional features of Weibo text, the technical solution of the present invention constructs a capsule network, which includes an input layer, a convolutional layer, a basic capsule layer, a convolutional capsule layer, a fully connected capsule layer, a capsule average pooling layer, and an output layer. Three types of convolution kernels are used in the convolutional layer, namely 3*300, 4*300, and 5*300. The use of three types of convolution kernels is to enable the model to better learn the feature information between Weibo text sentences. B: Training the capsule network. The Weibo text dataset of Weibo comments is split into a training set and a test set in a 4:1 ratio. The capsule network is trained on the training set. In order to prevent overfitting during the training process, the dropout rate is set to 0.5, that is, a part of the training parameters are randomly discarded in each iteration. The capsule network uses the Adam algorithm to update the parameters of each layer of the network, and the number of iterations is 100. After the capsule network model reaches a certain accuracy, the model is tested using the test set to check the model's prediction accuracy. C: Using the trained capsule network model to extract the emotional features of Weibo text. The microblog text represented by m n*k dimensional matrices is input into the trained capsule network. After extraction by the convolution layer, the learned local emotional features are not extracted again by the pooling layer, but are directly stored in the capsule layer, so that the position features between words can be better retained. Afterwards, the capsule network uses a dynamic routing algorithm to learn features. At the same time, the capsule network model of the embodiment of the present invention will retain the features of the emotional sentences marked before. Finally, the features learned by the three convolution kernels are all mapped to the capsule average pooling layer. In step five, the overall emotional features of a single microblog text have the same word vector representation as the local emotional features of the sentence. Combining the learned overall and local emotional features, the final emotional classification prediction results of a single microblog text to be classified can be obtained by three methods.

[0042] In another example, a method for emotion recognition of question-and-answer text is provided, which can also be applied to this embodiment. The method includes: processing the target question-and-answer text to extract multiple question-and-answer text pairs corresponding to all preset topics, each question-and-answer text pair including a question text and a corresponding answer text. In this step, diagnostic text or audio recording can be obtained through the diagnostic process of the medical interview or depression rating scale, and then converted into multiple rounds of question-and-answer text. A round of question-and-answer text is a question-and-answer text pair (Qi, Ai), including question text Qi and answer text Ai, where i∈[0,l], l is the number of question-and-answer text pairs. Many question-and-answer pairs will be generated during a depression assessment process, and not all of these question-and-answer pairs play a positive role in the identification of depression. Question-and-answer pairs that have little or no relevance to depression identification may become interference information, affecting the accuracy and efficiency of depression identification. Therefore, after obtaining the question-and-answer text, question-and-answer pair screening must be performed first. Inspired by scales used by doctors to screen for depression and relevant literature, the predefined topics here include: argue, proud, sleep, study, emotion, depression, and PTSD (post-traumatic stress disorder). For example, corresponding questions could be, "What was the reason / time for your last argument?", "Are there anything you're proud of?", "How do you sleep when you wake up?", "How is your learning ability?", "How is your emotional control?", "Have you ever been diagnosed with depression?", and "Have you ever been diagnosed with post-traumatic stress disorder?" Specifically, the target question-answer text can be processed in the following manner: all question-answer pairs in the target question-answer text are identified. For each question-answer pair, based on the similarity of the textual emotions, it is determined whether the topic expressed in the pair corresponds to one of the predefined topics. If so, the pair is designated as a question-answer pair corresponding to the predefined topic. To select question-answer pairs corresponding to the predefined topic, the MPNet (Masked and Permuted Pretraining for Language Understanding) model can be used to calculate sentence similarity, or manually constructed regular expressions can be used for matching. For each pre-defined topic, before converting all corresponding question-answer text pairs into feature vectors, the process also includes normalizing the question and answer texts in each pair using at least one of the following methods: restoring abbreviations in the question and answer texts; restoring parts of speech in the question and answer texts; removing stop words in the question and answer texts; and removing punctuation in the question and answer texts. The selected question-answer text pairs also require text preprocessing to normalize the text for input into the deep learning model.Here, all the methods can be used to process each question-answer text pair in sequence according to the above order. For each preset topic, all the question-answer text pairs corresponding to the preset topic are converted into feature vectors, and a question text vector sequence and an answer text vector sequence corresponding to the preset topic are formed. In this step, for each preset topic, a question text vector sequence and an answer text vector sequence corresponding to the preset topic are formed in the following way: all the question-answer text pairs corresponding to the preset topic are input into a pre-trained BERT model for vector conversion to output a question text vector sequence and an answer text vector sequence.

[0043] This embodiment also provides an optional method for determining the emotions of a character based on the performance information corresponding to the character, including: obtaining words in the performance information for describing the character's actions and / or expressions, or obtaining the language of the character in the performance information, and obtaining words from the language for indicating the character's emotions; dividing the words into positive words, neutral words, and negative words, wherein the positive words correspond to a first emotion, the neutral words correspond to a second word, and the negative words correspond to a third emotion, and the first emotion, the second emotion, and the third emotion are different emotions; and determining the emotions of the character based on the words.

[0044] All emotion distributions can include the above three emotions. Of course, in addition to the above three emotions, multiple emotions can also be included. The classification of emotion types can be set according to needs, but no matter how it is set, it should include the above three emotions, namely the first emotion, the second emotion and the third emotion.

[0045] Figure 2 is a schematic diagram of two-dimensional coordinates of emotion points according to an embodiment of the present application, such as Figure 2 As shown, the vertical axis can have four values, and the horizontal axis shows time in 30-second increments. Connecting the points on the two-dimensional coordinates forms a mood curve. A mood curve can be generated for each character. Points on a character's mood curve can also be labeled with the current event read from the script, making it easier to clearly display the reasons for changes in the mood curve. Figure 3 is a schematic diagram of the two-dimensional coordinates of the emotional points of character A related to the event according to an embodiment of the present application, such as Figure 3 As shown, the emotional changes of character A are written from the script. After being drawn into a two-dimensional coordinate system, events related to the emotional changes of character A are read from the script and displayed on the two-dimensional coordinate system, making the two-dimensional coordinate system more intuitive.

[0046] In an optional embodiment, a complete shooting outline may be generated according to the above steps. This method may be referred to as a method for automatically generating a shooting outline based on a script. The method may include the following steps:

[0047] Obtaining a script, and dividing the script into different chapters according to words used to indicate chapters in the script;

[0048] For each chapter, obtain text paragraphs of different scenes in the chapter, where the scenes are shooting scenes;

[0049] Obtaining a character in a text paragraph in a shooting scene and performance information of the character; wherein the performance information includes at least one of the following: language, action, and expression;

[0050] determining the character's emotion based on the performance information;

[0051] Obtaining lens position information in the shooting scene, wherein the lens position information is used to indicate a relative positional relationship between a camera or a virtual camera and the person when shooting the person, wherein there are one or more cameras or virtual cameras;

[0052] Displaying the character, the performance information, the emotion, and the camera position information under the shooting scene;

[0053] Arranging all the shooting scenes in the script that display the thought, the performance information, the emotion, and the camera position information according to a timeline;

[0054] A table is generated for the arranged shooting scenes, wherein the columns of the table are different shooting scenes, and the multiple rows under the columns are the characters in the shooting scene, the performance information of the characters, the emotions of the characters and the lens position information.

[0055] The above steps can automatically generate a shooting script, which avoids the time required for the shooting crew to read and understand the script, and to a certain extent provides a script basis for improving shooting efficiency.

[0056] The generated shooting script may be in a table format, and the table header content may be as follows:

[0057]

[0058]

[0059] As an additional implementation, the frequency of emotion changes can also be determined to determine whether the dramatic conflict in the scene is sufficient. That is, this implementation can also include the following steps: obtaining a two-dimensional coordinate system for a scene; obtaining all emotions in the two-dimensional coordinate system and determining the number of times the emotions change on the timeline, wherein the changes are changes between the first emotion, the second emotion, and the third emotion; obtaining the duration of the scene when it was captured as a video; determining the frequency of emotion changes based on the duration and the number of times; and displaying the two-dimensional coordinate system using different colors, wherein different frequencies of change correspond to different colors.

[0060] As another implementation that can be added, when the frequency of change of the emotion is lower than a first threshold or the frequency of change of the emotion is higher than a second threshold, a prompt message is displayed, wherein the prompt message is used to indicate that the frequency of change of the emotion is too slow or too fast.

[0061] The above-mentioned implementation solves the problem of subjective script evaluation caused by the inability to objectively display the conflicts in the script in the related art, thereby being able to objectively display the emotional changes in the script and providing objective data for evaluating the highlights in the script.

[0062] In this embodiment, an electronic device is provided, including a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program to execute the method in the above embodiment.

[0063] The above program can be run in the processor, or it can be stored in the memory (or computer-readable medium), which includes permanent and non-permanent, removable and non-removable media and can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0064] These computer programs can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps of the functions specified in one or more blocks can be implemented by different modules corresponding to different steps.

[0065] This embodiment provides such a device or system. The system is called a system for determining the frequency of dramatic conflict based on a script and is applied to software. The software includes the following modules: an acquisition module for acquiring a text paragraph from a script, wherein the text paragraph is a text paragraph under the same shooting scene, and the text paragraph describes at least one character and the performance information corresponding to the character, wherein the performance information includes at least one of the following: language, action, and expression; a determination module for determining the emotion of a character based on the performance information corresponding to the character, wherein the emotion is determined based on the text description in the performance information, and the emotion is a preset emotion; an arrangement module for acquiring the time axis corresponding to the performance information and arranging the emotions of the character in a two-dimensional coordinate system according to the time order on the time axis; wherein the horizontal axis of the two-dimensional coordinate system is the time axis, and the vertical axis of the two-dimensional coordinate system is the emotion, and different emotions have different positions on the vertical axis; a display module for displaying the two-dimensional coordinate system, wherein the displayed two-dimensional coordinate system is used as a shooting outline.

[0066] The system or device is used to implement the functions of the method in the above-mentioned embodiment. Each module in the system or device corresponds to each step in the method, which has been explained in the method and will not be repeated here.

[0067] Optionally, the acquisition module is used to: divide the script into different chapters according to the text used to identify chapters in the script; obtain the corresponding scene in each chapter, wherein the scene is a shooting scene; and use the text in the same scene as the text paragraph.

[0068] Optionally, the display module is also used to: generate a corresponding two-dimensional coordinate system for each scene in all chapters of the script; arrange all the generated two-dimensional coordinate systems in chronological order according to the scenes corresponding to the two-dimensional coordinate systems in the chapter; display the arranged two-dimensional coordinate systems in the chapter; arrange all chapters in chronological order, and display the two-dimensional coordinate systems corresponding to all the arranged chapters.

[0069] Optionally, the determination module is used to: obtain words from the performance information used to describe the character's actions and / or expressions, or obtain the language of the character in the performance information, and obtain words from the language used to indicate the character's emotions; divide the words into positive words, neutral words, and negative words, wherein the positive words correspond to a first emotion, the neutral words correspond to a second word, and the negative words correspond to a third emotion, and the first emotion, the second emotion, and the third emotion are different emotions; determine the character's emotions based on the words.

[0070] Optionally, the determination module is also used to: obtain a two-dimensional coordinate system in a scene; obtain all emotions in the two-dimensional coordinate system, and determine the number of times the emotion changes on the time axis, wherein the change is the change between the first emotion, the second emotion and the third emotion; obtain the duration of the scene when it is captured as a video; determine the frequency of change of the emotion based on the duration and the number of times; and display the two-dimensional coordinate system using different colors, wherein different change frequencies correspond to different colors.

[0071] As another embodiment that can be added, the display module is also used to: display a prompt message when the frequency of change of the emotion is lower than a first threshold or the frequency of change of the emotion is higher than a second threshold, wherein the prompt message is used to indicate that the frequency of change of the emotion is too slow or too fast.

[0072] Through the above implementation, the problem of subjective script evaluation caused by the inability to objectively display the conflicts in the script in the related art is solved, so that the emotional changes in the script can be objectively displayed, providing objective data for evaluating the highlights in the script.

[0073] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for determining the frequency of dramatic conflict based on a script, applied to software for executing the method, characterized in that: The method comprises the following steps: Obtaining a text paragraph from a script, wherein the text paragraph is a text paragraph for a same shooting scene, and the text paragraph describes at least one character and performance information corresponding to the character, wherein the performance information includes at least one of the following: language, action, and expression; Determining the emotions of a character based on performance information corresponding to the character, wherein the emotions are several preset emotions; obtaining words from the performance information for describing the character's actions and / or expressions, or obtaining the language of the character in the performance information, and obtaining words from the language for indicating the character's emotions; classifying the words into positive words, neutral words, and negative words, wherein the positive words correspond to a first emotion, the neutral words correspond to a second emotion, and the negative words correspond to a third emotion, wherein the first emotion, the second emotion, and the third emotion are different emotions; and determining the emotion of the character based on the words; Obtaining a time axis corresponding to the performance information, and arranging the emotions of the character on a two-dimensional coordinate system according to the time sequence on the time axis; wherein the horizontal axis of the two-dimensional coordinate system is the time axis, the vertical axis of the two-dimensional coordinate system is the emotion, and different emotions have different positions on the vertical axis; The two-dimensional coordinate system is displayed, wherein the displayed two-dimensional coordinate system is used as a shooting outline, and the emotions on the two-dimensional coordinate system are used as a basis for determining the dramatic conflict in the scene; wherein, the two-dimensional coordinate system in the scene is obtained; the emotions in the two-dimensional coordinate system are obtained, and the number of times the emotions occur between the first emotion, the second emotion, and the third emotion on the time axis is determined; the duration of the scene when it is shot into a video is obtained; the frequency of change of the emotions is determined based on the duration and the number of times; the two-dimensional coordinate system is displayed using different colors, and different change frequencies correspond to different colors; the frequency of change of the emotions is used to determine whether the dramatic conflict in the scene is sufficient; It also includes: for each chapter in the script, obtaining text paragraphs of different scenes in the chapter, where the scenes are shooting scenes; obtaining characters in the text paragraphs under a shooting scene and the performance information of the characters; obtaining lens position information under the shooting scene, where the lens position information is used to indicate the relative position relationship between the camera or virtual camera and the characters when shooting the characters; displaying the characters, performance information, the characters' emotions and lens position information under the shooting scene; arranging all the shooting scenes in the script that display characters, performance information, emotions and lens position information according to the timeline; generating a table for the arranged shooting scenes, where the columns of the table are different shooting scenes, and the multiple rows under the columns are the characters in the shooting scene, the characters' performance information, the characters' emotions and the lens position information.

2. The method according to claim 1, characterized in that Also includes: Generate a corresponding two-dimensional coordinate system for each scene in all chapters of the script; Arrange all generated two-dimensional coordinate systems in chronological order according to the scenes corresponding to the two-dimensional coordinate systems in the chapter; The arranged two-dimensional coordinate system is displayed in this section; Arrange all chapters in chronological order, and display the two-dimensional coordinate system corresponding to all the arranged chapters.

3. An electronic device comprising a memory and a processor; wherein: The memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method steps according to any one of claims 1 to 2.

4. A readable storage medium having computer instructions stored thereon, wherein: When the computer instructions are executed by a processor, the method steps described in any one of claims 1 to 2 are implemented.

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