WO2020147395A1 - 基于情感的文本分类处理方法、装置和计算机设备 - Google Patents

基于情感的文本分类处理方法、装置和计算机设备 Download PDF

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WO2020147395A1
WO2020147395A1 PCT/CN2019/117161 CN2019117161W WO2020147395A1 WO 2020147395 A1 WO2020147395 A1 WO 2020147395A1 CN 2019117161 W CN2019117161 W CN 2019117161W WO 2020147395 A1 WO2020147395 A1 WO 2020147395A1
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emotion
question
text
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classified
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French (fr)
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金戈
徐亮
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis

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  • This application relates to an emotion-based text classification processing method, device, computer equipment and storage medium.
  • sentiment classification is usually performed on all the collected texts uniformly.
  • text content corresponds to different problems, and different problems may reflect different emotions.
  • the unified sentiment classification of all text content cannot accurately reflect the sentiment expression under different problems, resulting in inaccurate results of sentiment classification. Therefore, how to improve the accuracy of sentiment classification has become a technical problem that needs to be solved at present.
  • an emotion-based text classification processing method is provided.
  • An emotion-based text classification processing method includes:
  • the emotion classification task includes a text identifier to be classified
  • the sentiment classification model including a plurality of sub-models corresponding to the question
  • An emotion-based text classification processing device includes:
  • the task acquisition module is used to acquire the sentiment classification task, and the sentiment classification task includes the text identification to be classified;
  • a text obtaining module configured to obtain a corresponding text to be classified according to the text identifier to be classified, where the text to be classified includes multiple questions and question answers corresponding to the questions;
  • the model calling module is used to call the corresponding emotion classification model according to the emotion classification task, the emotion classification model includes a plurality of sub-models corresponding to the question; the question answer corresponding to the question is input to the question corresponding The sub-model of, through the sub-model operation, output the emotional score corresponding to the question answer; and
  • the emotion type recognition module is used to recognize the emotion type corresponding to the text to be classified according to the emotion scores corresponding to the multiple question answers.
  • a computer device includes a memory and one or more processors.
  • the memory stores computer-readable instructions.
  • the one or more processors are executed The following steps:
  • the emotion classification task includes a text identifier to be classified
  • the sentiment classification model including a plurality of sub-models corresponding to the question
  • One or more non-volatile computer-readable storage media storing computer-readable instructions.
  • the computer-readable instructions When executed by one or more processors, the one or more processors perform the following steps:
  • the emotion classification task includes a text identifier to be classified
  • the sentiment classification model including a plurality of sub-models corresponding to the question
  • Fig. 1 is an application environment diagram of an emotion-based text classification processing method according to one or more embodiments.
  • Fig. 2 is a schematic flowchart of an emotion-based text classification processing method according to one or more embodiments.
  • Fig. 3 is a schematic flowchart of the steps of extracting subject information according to emotion types in one or more embodiments.
  • Fig. 4 is a block diagram of an emotion-based text classification processing apparatus according to one or more embodiments.
  • Figure 5 is a block diagram of a computer device in accordance with one or more embodiments.
  • the emotion-based text classification processing method provided in this application can be applied to the application environment shown in FIG. 1.
  • the terminal 102 communicates with the server 104 through the network through the network.
  • the server 104 obtains the sentiment classification task initiated by the terminal 102.
  • the sentiment classification task includes the text identification to be classified.
  • the server 104 obtains the corresponding text to be classified according to the text identification to be classified, and calls the corresponding sentiment classification model according to the obtained sentiment classification task.
  • the server 104 inputs the question and question answer in the text to be classified into the sub-model of the emotion classification model, and outputs the emotion score corresponding to the question through the sub-model calculation.
  • the server 104 recognizes the emotion type corresponding to the text to be classified according to the emotion scores corresponding to the multiple questions.
  • the terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.
  • the server 104 may be implemented by an independent server or a server cluster composed of multiple servers.
  • an emotion-based text classification processing method is provided. Taking the method applied to the server 104 in FIG. 1 as an example for description, the method includes the following steps:
  • Step 202 Obtain an emotion classification task, and the emotion classification task includes a text identifier to be classified.
  • the server obtains the emotion classification task, analyzes the obtained emotion classification task, and obtains the text identification to be classified included in the emotion classification task.
  • the emotion classification task can be created by the terminal and uploaded to the server.
  • the sentiment classification task includes the text identification to be classified, and the text identification to be classified points to the corresponding text to be classified.
  • the text identifiers to be classified can include multiple types. In one of the embodiments, the text identifier to be classified may be the file name of the text to be classified.
  • the server can perform sentiment classification tasks, and perform sentiment classification processing on the text to be classified corresponding to the text identifier to be classified.
  • Step 204 Obtain the corresponding text to be classified according to the identifier of the text to be classified.
  • the text to be classified includes multiple questions and question answers corresponding to the questions.
  • the server may obtain the corresponding text to be classified according to the identifier of the text to be classified. Specifically, the server may obtain the mapping relationship between the text identification to be classified and the text to be classified, and use the mapping relationship to obtain the text to be classified corresponding to the text identification to be classified.
  • the text to be classified includes multiple questions and corresponding question answers.
  • the server can ask different questions for different users, or ask different users the same question.
  • the question answer is the answer made by the user for each question.
  • different questions can be asked for different interviewers, and the interviewer's answer can be received as the answer to the question corresponding to the question, so as to avoid the interviewer's private communication and more truly reflect the interviewer's adaptability.
  • the server can randomly select questions from preset questions to ask questions, or select related questions to ask questions based on the interviewer's answers.
  • the server may ask the same questions for different respondents, and receive answers to the questions from the respondents, so as to understand the respondent's emotional tendency toward these questions.
  • Step 206 Invoke a corresponding emotion classification model according to the emotion classification task.
  • the emotion classification model includes a plurality of sub-models corresponding to the question.
  • the server can call the corresponding sentiment classification model according to the sentiment classification task.
  • the sentiment classification model is a model obtained through training for sentiment classification of questions and question answer texts.
  • the sentiment classification model can be trained by a variety of classification models, such as the Fasttext model.
  • the sentiment classification model includes multiple sub-models, and different questions can correspond to different sub-models.
  • the server can call the sub-model corresponding to the question in the sentiment classification model according to the sentiment classification task to perform sentiment classification processing on the response to the question.
  • Step 208 Input the question answer corresponding to the question into the sub-model corresponding to the question, and output the emotional score corresponding to the question answer through the sub-model calculation.
  • the server inputs the question answer corresponding to the question in the text to be classified into the sub-model corresponding to the question, and calls the sub-model to perform sentiment classification on the question answer.
  • the sentiment score can reflect the emotional tendency of the question answer to the question.
  • the server obtains the probability of each emotion label corresponding to the question answer by calling the sub-model for calculation. Calculate according to the probability of the corresponding sentiment label, obtain the sentiment score corresponding to the question answer, and output the sentiment score corresponding to the question answer. For example, using the Fasttext model to perform sentiment classification on the text to be classified.
  • the server inputs the question answers in the text to be classified into the sub-models of the Fasttext model corresponding to the questions, and the sub-models perform sentiment classification processing on the question responses to obtain the probabilities corresponding to multiple sentiment labels.
  • the emotion label may include positive and negative
  • the result of the sentiment classification of the answer to the question is: a positive probability of 68%, and a negative probability of 32%.
  • the sentiment score corresponding to the corresponding question answer is calculated. For example, when the full score is 10 points, the sentiment score is 7 points.
  • Step 210 Identify the emotion type corresponding to the text to be classified according to the emotion scores corresponding to the multiple question answers.
  • the server synthesizes the sentiment scores corresponding to the multiple question answers, recognizes the sentiment type corresponding to the text to be classified according to the sentiment scores corresponding to the multiple question responses, and completes the sentiment classification task of emotional classification of the text to be classified.
  • multiple questions corresponding to the text to be classified and the question answer corresponding to each question are obtained according to the text identification to be classified, and the response in the sentiment classification model is called
  • Multiple sub-models classify the sentiment of each question answer one by one, and identify the sentiment type corresponding to the text to be classified according to the obtained sentiment score corresponding to each question answer.
  • the sentiment classification of the question answer is carried out one by one, and then the sentiment score corresponding to each question answer is synthesized to identify the sentiment type corresponding to the text to be classified.
  • it Compared with the unified sentiment classification of all texts in the traditional way, it fully combines the questions and carries out sentiment classification to the corresponding question answers, which effectively improves the accuracy of sentiment classification of the text.
  • identifying the sentiment type corresponding to the text to be classified according to the sentiment scores corresponding to multiple question answers includes: obtaining the weight corresponding to each question; calculating according to the weight and the sentiment score corresponding to the question answer to obtain Modified score; add up the modified scores corresponding to the answers to multiple questions to obtain the total emotional score; identify the emotional type corresponding to the text to be classified according to the emotional total score.
  • the server obtains the weight corresponding to each question. Since each question has a different importance to the degree of classification of the classified text, in order to balance the impact of each question on the degree of sentiment classification, a weight is set for each question.
  • the user can preset the corresponding importance for each question, and the server sets the corresponding weight of each question according to the importance of each question in all the questions.
  • the weight of important questions can be higher, and the weight of simple questions can be lower.
  • the server calculates according to the weight and the emotional score corresponding to each question answer, and obtains the modified score corresponding to each question answer.
  • the revised score can objectively reflect the emotion of the corresponding question answer in the text to be classified.
  • the weight of the question about identity can be less than the weight of the question about personal skills and job search intentions. Therefore, according to the sentiment scores of the question answers of the three types of questions, it is necessary to recalculate the corresponding weights of the questions to obtain the revised scores corresponding to the question answers.
  • the server accumulates the modified scores corresponding to the multiple question answers calculated to obtain the total emotional score.
  • the total sentiment score is the total sentiment score of the text to be classified into the question.
  • the server recognizes the emotion type corresponding to the text to be classified according to the total emotional score, and completes the emotion classification task of text classification of the text to be classified.
  • the server obtains the weight corresponding to each question, calculates the revised score corresponding to each question answer, and accumulates the revised scores corresponding to the multiple question answers, to obtain the total emotion corresponding to the text to be classified.
  • the score is used to identify the emotional type corresponding to the text to be classified through the emotional total score.
  • the above emotion-based text classification processing method before obtaining the emotion classification task, further includes: obtaining reply data corresponding to the question; identifying the data type corresponding to the reply data; when the data type is a voice type, The reply data of voice type is converted into reply data of text type; the text to be classified is generated according to the reply data of text type and the question.
  • the user's response data to the question can include multiple data types. For example, it can include text type and voice type.
  • the server can obtain the reply data uploaded by the user in response to the question. After obtaining the reply data corresponding to the question, the server can determine the data type of the reply data. If the data type is a text type, the text type response data is directly used as the question response, and the text to be classified is generated by combining multiple questions and the corresponding question responses of the questions. If the reply data is voice type data, the server can convert the voice data into text type reply data, use the reply data as a question reply, and generate a text to be classified.
  • the server may also receive voice-type reply data.
  • voice type response data By converting voice type response data into text type response data, the text to be classified is generated based on the question and text type response data, which enriches the data types of compatible question responses and facilitates users to answer questions in multiple ways.
  • the question and the corresponding question answer are input to the corresponding sub-model, and the sub-model calculation is used to output the sentiment score corresponding to the question, including: segmenting the question answer to obtain multiple words; calling according to the question Corresponding sub-models; use the sub-models to classify the emotions of words and obtain the probability values corresponding to multiple emotional expressions; determine the emotional scores corresponding to the question answers according to the probability values.
  • the server can use multiple methods to segment the answers to the questions in the classified text to obtain multiple words. Specifically, the server may use one or a combination of string matching, comprehension, statistics and other methods to segment the answer to the question.
  • the string matching method refers to matching the question answer with the entry in the preset dictionary. If the string is found in the dictionary, it is considered that the match is successful, that is, a word is recognized.
  • the way of string matching can include forward string matching and reverse string matching.
  • the way of understanding means that the server performs semantic analysis and syntactic analysis when cutting the question answer, and uses semantic information and syntactic information to process the ambiguity that occurs when the words are cut.
  • the statistical method means that the server counts the frequency of the combination of adjacent co-occurring words in the question answer, and performs word segmentation based on their co-occurrence information.
  • the server may also introduce N-gram vectors when segmenting words. The obtained vector features are used to determine the word sequence after word segmentation of the question answer, which more accurately reflects the content of the question answer, and effectively improves the accuracy of sentiment classification of the classified text.
  • Each question corresponds to a sub-model.
  • the server obtains the mapping relationship between the problem and the sub-model, and calls the sub-model corresponding to the problem according to the mapping relationship.
  • the server inputs the multiple words obtained by word segmentation into the sub-model, and uses the sub-model to classify the words corresponding to the question answer, and obtains the probability values corresponding to the multiple preset emotion labels.
  • the probability value indicates the probability that the emotion of the answer to the question belongs to the corresponding emotion label.
  • the server may calculate the emotion score corresponding to the question answer according to the probability value corresponding to each of the multiple emotion tags.
  • the sub-model corresponding to the question is called to classify the obtained words, and the sentiment score corresponding to the question answer is obtained.
  • the corresponding sub-model is called for the specific content of each question, and the sentiment score corresponding to the question answer is calculated, which effectively improves the accuracy of sentiment classification for the text to be classified.
  • the server cleans the text to be classified. For example, delete the text to be classified that does not meet the preset rules. Then perform sentiment classification on the text to be classified after cleaning. Specifically, the server divides the question answer into words, and after obtaining multiple words, calls the corresponding sub-model according to the question. According to the needs of the sub-model, multiple words are sorted into the data form required by the sub-model. For example, the server can sort the words into a table form, and then input the words in the table form into the sub-model, and use the sub-model to classify the sentiment of the question answer to obtain the sentiment score corresponding to the question answer.
  • the text to be classified is obtained, the text to be classified is cleaned up, the illegal text to be classified is eliminated, the words obtained by word segmentation are sorted, and the data form corresponding to the sub-model is obtained and input into the sub-model. Effectively improve the efficiency of sentiment classification for classified text.
  • the above method further includes: extracting subject information according to the emotion type. As shown in Figure 3, this step specifically includes:
  • Step 302 Acquire preset types of emotional needs.
  • Step 304 Match the classified emotion type with the emotion demand type to obtain the corresponding first matching degree.
  • Step 306 Filter out target emotion types that meet preset conditions according to the first matching degree.
  • Step 308 Extract subject information corresponding to the target emotion type.
  • the server can obtain the type of emotional demand preset by the user.
  • the type of emotional demand refers to the emotional type that meets the needs of the user, and can be preset according to the actual needs of the user.
  • the server may use multiple methods to match the emotion type obtained after emotion classification of the text to be classified with the emotion demand type. Specifically, the server may match the emotional types obtained after classification with the emotional demand types one by one, and may also call multiple threads to match the emotional type and the emotional demand type in parallel. After the server matches the emotion type with the emotion demand type, the first matching degree corresponding to the emotion type is obtained.
  • the server screens the first matching degrees corresponding to multiple emotion types, and screens out target emotion types that meet preset conditions.
  • the preset condition may be a condition preset by the user, such as filtering out the emotion types whose first matching degree is greater than a threshold.
  • the server extracts subject information corresponding to the target emotion type that meets the preset conditions to obtain subject information that meets the emotional demand type.
  • the server classifies the text to be classified by the interviewer and obtains the emotion type corresponding to the interviewer.
  • the server can obtain the emotion demand type corresponding to the job position, and compare the emotion type of the interviewee with the position Match the types of emotional needs and get the first degree of matching between multiple interviewers and the position.
  • the server screens according to the first matching degree, and screens out the target emotion type whose first matching degree meets the preset conditions, and the target emotion type is the type that meets the emotional needs of the post.
  • the server extracts the subject information corresponding to the target emotion type that meets the conditions, and obtains the subject information that meets the job conditions.
  • the target emotional type that matches the emotional needs of the job is selected, and the interviewers who meet the needs of the recruitment position are initially selected from multiple interviewers according to the emotional type, which effectively improves the efficiency and accuracy of the interview Sex.
  • the target emotion type that meets the preset condition is screened according to the first matching degree, and the emotion type is effectively used for screening to obtain the subject information that meets the conditions. Enriched target screening methods and improved screening efficiency.
  • the above method further includes: obtaining a variety of product information, the product information includes product types; The emotion type corresponding to the classified text is matched to obtain the second matching degree; when the second matching degree is greater than the predetermined value, the product information corresponding to the product type is marked as the target product information; the terminal identification corresponding to the text to be classified is extracted, and the target product The information is pushed to the terminal corresponding to the terminal identifier.
  • the server obtains product information corresponding to multiple products, and the product information includes the product type corresponding to the product.
  • Multiple products can include products of different product types.
  • the fund products include high-risk and high-yield products as well as low-risk, low-yield products.
  • the server can use multiple methods to match the product types of multiple products with the emotional types corresponding to the text to be classified. Specifically, the server may sequentially match product types corresponding to multiple products with emotion types, or call multiple threads to match the product types and emotion types in parallel to obtain the second matching degree between the product types and emotion types. When the second matching degree is greater than the predetermined value, the product information corresponding to the product type is marked as the target product information.
  • the predetermined value is a value preset by the user.
  • the second matching degree is greater than the predetermined value, it means that the product corresponding to the product type matches the user corresponding to the emotion type. For example, high-risk and high-yield products are more likely to match users who tend to be adventurous, and low-risk and low-yield products are more likely to match users who tend to be conservative.
  • the server extracts the terminal identifier corresponding to the terminal uploading the text to be classified, pushes the target product information to the terminal corresponding to the terminal identifier, and completes the push of product information that matches the emotional type.
  • target product information that matches the emotion type is pushed to the user, which effectively improves the accuracy of corresponding information push.
  • emotional demand types can also be acquired, and the classified emotional types can be matched with emotional demand types to obtain Corresponding to the first matching degree, the emotion type that meets the preset condition is filtered out according to the first matching degree.
  • the server then matches multiple product types with the selected emotion types to obtain the corresponding second matching degree, marks the product information corresponding to the product type with the second matching degree greater than the predetermined value as target product information, and pushes the target product information To the terminal corresponding to the emotion type.
  • the server can use the emotion type to screen users, and screen out users who meet the type of emotional needs. For example, the server can filter out users who have a positive intention to purchase products according to their emotion types, and then match the product types with the selected user emotion types, and filter out product types that match the user's emotion types from multiple product types. Reach the effect of pushing product information that matches the user's emotional type to users with purchase intentions.
  • the combination of screening of emotion types and screening of product types effectively improves the efficiency and accuracy of product information push.
  • the above method further includes: establishing a general sentiment classification model, the general sentiment classification model includes a general sub-model; acquiring a training data set, the training data set includes multiple training texts and standard sentiment scores corresponding to the training texts; Call the general sub-model to perform operations on the training text to obtain the training emotional score; compare the training emotional score with the standard emotional score; adjust the general sub-model according to the comparison result to obtain the target sub-model.
  • the server can establish a general sentiment classification model, and the general sentiment classification model can adopt the Fasttext model.
  • the server receives the training data set, and the training data set may include multiple training texts and standard sentiment scores corresponding to the training texts.
  • the training text includes standard questions and answers to questions corresponding to the standard questions. Each standard question can correspond to multiple question answers, and the corresponding standard sentiment scores for multiple question answers.
  • the server can use the training data set to train the general sentiment classification model. Specifically, the server invokes the general sub-model in the general sentiment classification model corresponding to the standard question, inputs the corresponding question answer into the general sub-model, and outputs the training sentiment score through the general sub-model calculation.
  • the server may compare the training sentiment score with the standard sentiment score, and adjust the corresponding general sub-model according to the comparison result.
  • the server trains multiple general sub-models of the general sentiment classification model by using standard questions, question answers and standard sentiment scores in multiple training texts to obtain the target sentiment classification model.
  • the server uses the training data set to train the general sentiment classification model to obtain a target sentiment classification model including multiple target sub-models, which effectively improves the accuracy of the target sentiment classification model for classifying text based on sentiment treatment .
  • an emotion-based text classification processing device which includes: a task acquisition module 402, a text acquisition module 404, a model calling module 406, and an emotion type recognition module 408, wherein:
  • the task acquisition module 402 is used to acquire the sentiment classification task, and the sentiment classification task includes the text identification to be classified.
  • the text obtaining module 404 is configured to obtain the corresponding text to be classified according to the identifier of the text to be classified.
  • the text to be classified includes multiple questions and question answers corresponding to the questions.
  • the model calling module 406 is used to call the corresponding sentiment classification model according to the sentiment classification task.
  • the sentiment classification model includes a plurality of sub-models corresponding to the question; the question answer corresponding to the question is input into the corresponding sub-model of the question, and the sub-model calculation Output the emotional score corresponding to the answer to the question.
  • the emotion type recognition module 408 is configured to recognize the emotion type corresponding to the text to be classified according to the emotion scores corresponding to the multiple question answers.
  • the above-mentioned model calling module 406 is also used to segment the answer to the question to obtain multiple words; call the corresponding sub-model according to the question; use the sub-model to classify the words and obtain the corresponding emotion tags Probability value: Determine the sentiment score corresponding to the question answer according to the probability value.
  • the above-mentioned device further includes a need type matching module for obtaining preset emotional need types; matching the classified emotional type with the emotional need type to obtain the first matching degree corresponding to the emotional type; The first matching degree screens out the target emotion type that meets the preset conditions; extracts the subject information corresponding to the target emotion type.
  • the above-mentioned device further includes a product push module for obtaining various product information
  • the product information includes the product type
  • the product type is matched with the emotion type corresponding to the text to be classified to obtain the second matching degree
  • the product information corresponding to the product type is marked as the target product information
  • the terminal identification corresponding to the text to be classified is extracted, and the target product information is pushed to the terminal corresponding to the terminal identification.
  • the above-mentioned emotion type recognition module 408 is also used to obtain the weight corresponding to each question; calculate according to the weight and the emotion score corresponding to the question answer to obtain the modified score; correspond to multiple question answers
  • the modified scores of is accumulated to obtain the total emotional score; the emotional type corresponding to the text to be classified is identified according to the total emotional score.
  • the above-mentioned task acquisition module 402 is also used to acquire the reply data corresponding to the question; identify the data type corresponding to the reply data; when the data type is a voice type, convert the reply data of the voice type into a text type. Reply data; generate text to be classified according to the reply data of the text type and the question.
  • the above-mentioned device further includes a model training module for establishing a general sentiment classification model, the general sentiment classification model includes a general sub-model; the training data set is obtained, and the training data set includes multiple training texts and corresponding training texts.
  • Standard sentiment score call the general sub-model to calculate the training text to obtain the training sentiment score; compare the training sentiment score with the standard sentiment score; adjust the general sub-model according to the comparison result to obtain the target sub-model .
  • the various modules in the above emotion-based text classification processing device can be implemented in whole or in part by software, hardware, and combinations thereof.
  • the above-mentioned modules may be embedded in the form of hardware or independent of the processor in the computer equipment, or may be stored in the memory of the computer equipment in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.
  • a computer device is provided.
  • the computer device may be a server, and its internal structure diagram may be as shown in FIG. 5.
  • the computer device includes a processor, memory, network interface, and database connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities.
  • the memory of the computer device includes a non-volatile storage medium and an internal memory.
  • the non-volatile storage medium stores an operating system, computer-readable instructions, and a database.
  • the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the non-volatile storage medium.
  • the database of the computer equipment is used to store emotion-based text classification processing data.
  • the network interface of the computer device is used to communicate with external terminals through a network connection.
  • the computer-readable instructions are executed by the processor to realize an emotion-based text classification processing method.
  • FIG. 5 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.
  • the specific computer device may Include more or less components than shown in the figure, or combine certain components, or have a different arrangement of components.
  • a computer device includes a memory and one or more processors.
  • the memory stores computer readable instructions.
  • the one or more processors execute the above method. step.
  • One or more non-volatile computer-readable storage media storing computer-readable instructions.
  • the computer-readable instructions execute A step of.
  • Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory can include random access memory (RAM) or external cache memory.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Channel (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

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Abstract

一种基于情感的文本分类处理方法，包括：获取情感分类任务，所述情感分类任务中包括待分类文本标识；根据所述待分类文本标识获取对应的待分类文本，所述待分类文本包括多个问题以及所述问题对应的问题答复；根据所述情感分类任务调用对应的情感分类模型，所述情感分类模型包括多个与所述问题对应的子模型；将所述问题对应的问题答复输入至所述问题对应的子模型，通过子模型运算，输出所述问题答复对应的情感分值；及根据多个问题答复对应的情感分值识别所述待分类文本对应的情感类型。

Description

基于情感的文本分类处理方法、装置和计算机设备
本申请要求于2019年01月17日提交至中国专利局，申请号为2019100428375，申请名称为“基于情感的文本分类处理方法、装置和计算机设备”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及一种基于情感的文本分类处理方法、装置、计算机设备和存储介质。
背景技术
随着计算机技术的发展，计算机技术逐渐被应用到对文本的情感分类处理中。例如，在AI(Artificial Intelligence，人工智能)面试中，通过对面试者的回答进行情感分类处理，得到面试者的情感态度，以便企业根据需求对面试者进行筛选。再例如，在问卷调查中，对被调查者的回答进行情感分类处理，以了解被调查者的情感倾向。
在传统方式中，通常是对采集到的所有文本统一进行情感分类。然而，发明人意识到，在很多情况下，文本内容对应有不同的问题，不同的问题对情感的反映程度可能是不同的。对所有文本内容统一进行情感分类不能准确反映不同问题下的情感表达，从而导致情感分类结果出现不准确的情况。因此，如何提高情感分类的准确性成为目前需要解决的一个技术问题。
发明内容
根据本申请公开的各种实施例，提供一种基于情感的文本分类处理方法、装置、计算机设备和存储介质。
一种基于情感的文本分类处理方法包括：
获取情感分类任务，所述情感分类任务中包括待分类文本标识；
根据所述待分类文本标识获取对应的待分类文本，所述待分类文本包括多个问题以及所述问题对应的问题答复；
根据所述情感分类任务调用对应的情感分类模型，所述情感分类模型包括多个与所述问题对应的子模型；
将所述问题对应的问题答复输入至所述问题对应的子模型，通过子模型运算，输出所述问题答复对应的情感分值；及
根据多个问题答复对应的情感分值识别所述待分类文本对应的情感类型。
一种基于情感的文本分类处理装置包括：
任务获取模块，用于获取情感分类任务，所述情感分类任务中包括待分类文本标识；
文本获取模块，用于根据所述待分类文本标识获取对应的待分类文本，所述待分类文本包括多个问题以及所述问题对应的问题答复；
模型调用模块，用于根据所述情感分类任务调用对应的情感分类模型，所述情感分类模型包括多个与所述问题对应的子模型；将所述问题对应的问题答复输入至所述问题对应的子模型，通过子模型运算，输出所述问题答复对应的情感分值；及
情感类型识别模块，用于根据多个问题答复对应的情感分值识别所述待分类文本对应的情感类型。
一种计算机设备，包括存储器和一个或多个处理器，所述存储器中储存有计算机可读指令，所述计算机可读指令被所述处理器执行时，使得所述一个或多个处理器执行以下步骤：
获取情感分类任务，所述情感分类任务中包括待分类文本标识；
根据所述待分类文本标识获取对应的待分类文本，所述待分类文本包括多个问题以及所述问题对应的问题答复；
根据所述情感分类任务调用对应的情感分类模型，所述情感分类模型包括多个与所述问题对应的子模型；
将所述问题对应的问题答复输入至所述问题对应的子模型，通过子模型运算，输出所述问题答复对应的情感分值；及
根据多个问题答复对应的情感分值识别所述待分类文本对应的情感类型。
一个或多个存储有计算机可读指令的非易失性计算机可读存储介质，计算机可读指令被一个或多个处理器执行时，使得一个或多个处理器执行以下步骤：
获取情感分类任务，所述情感分类任务中包括待分类文本标识；
根据所述待分类文本标识获取对应的待分类文本，所述待分类文本包括多个问题以及所述问题对应的问题答复；
根据所述情感分类任务调用对应的情感分类模型，所述情感分类模型包括多个与所述问题对应的子模型；
将所述问题对应的问题答复输入至所述问题对应的子模型，通过子模型运算，输出所述问题答复对应的情感分值；及
根据多个问题答复对应的情感分值识别所述待分类文本对应的情感类型。
本申请的一个或多个实施例的细节在下面的附图和描述中提出。本申请的其它特征和优点将从说明书、附图以及权利要求书变得明显。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其它的附图。
图1为根据一个或多个实施例中基于情感的文本分类处理方法的应用环境图。
图2为根据一个或多个实施例中基于情感的文本分类处理方法的流程示意图。
图3为根据一个或多个实施例中根据情感类型提取主体信息的步骤的流程示意图。
图4为根据一个或多个实施例中基于情感的文本分类处理装置的框图。
图5为根据一个或多个实施例中计算机设备的框图。
具体实施方式
为了使本申请的技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
本申请提供的基于情感的文本分类处理方法，可以应用于如图1所示的应用环境中。终端102通过网络与服务器104通过网络进行通信。服务器104获取终端102发起的情感分类任务，情感分类任务中包括待分类文本标识，服务器104根据待分类文本标识获取对应的待分类文本，根据获取到的情感分类任务调用对应的情感分类模型。服务器104将待分类文本中的问题及问题答复输入情感分类模型的子模型中，通过子模型运算，输出问题对应的情感分值。服务器104根据多个问题对应的情感分值识别待分类文本对应的情感类型。终端102可以但不限于是各种个人计算机、笔记本电脑、智能手机、平板电脑和便携式可穿戴设备，服务器104可以用独立的服务器或者是多个服务器组成的服务器集群来实现。
在其中一个实施例中，如图2所示，提供了一种基于情感的文本分类处理方法，以该方法应用于图1中的服务器104为例进行说明，包括以下步骤：
步骤202，获取情感分类任务，情感分类任务中包括待分类文本标识。
服务器获取情感分类任务，对获取到的情感分类任务进行解析，得到情感分类任务中包括的待分类文本标识。该情感分类任务可由终端创建、上传至服务器。情感分类任务中包括了待分类文本标识，待分类文本标识指向对应的待分类文本。待分类文本标识可以包括多种类型。在其中一个实施例中，待分类文本标识可以是待分类文本的文件名。服务器可以执行情感分类任务，对待分类文本标识对应的待分类文本进行情感分类处理。
步骤204，根据待分类文本标识获取对应的待分类文本，待分类文本包括多个问题以及问题对应的问题答复。
服务器可以根据待分类文本标识获取对应的待分类文本。具体的，服务器可以获取待分类文本标识与待分类文本之间的映射关系，利用映射关系获取与待分类文本标识对应的待分类文本。待分类文本中包括多个问题以及对应的问 题答复。
服务器可以针对不同用户提出不同问题，也可以对不同用户提出相同问题。问题答复是由用户针对每个问题对应做出的答复意见。例如，在人工智能面试过程中，可以针对不同的面试者提出不同的问题，接收面试者的回答作为问题对应的问题答复，以避免面试者私下交流，更真实的反映面试者的应变能力。提出问题的方式可以是多样的。服务器可以从预设问题中随机挑选问题进行提问，也可以根据面试者的回答挑选相关的问题进行提问。再例如，在问卷调查过程中，服务器可以针对不同的被调查者提出相同的问题，接收被调查者的问题答复，以了解被调查者对该些问题的情感倾向。
步骤206，根据情感分类任务调用对应的情感分类模型，情感分类模型包括多个与问题对应的子模型。
服务器可以根据情感分类任务调用对应的情感分类模型，情感分类模型是通过训练得到的用于对问题和问题答复文本进行情感分类的模型。情感分类模型可以由多种分类模型训练得到，比如Fasttext模型。情感分类模型中包括多个子模型，不同的问题可以对应不同的子模型。服务器可以根据情感分类任务调用情感分类模型中问题对应的子模型对问题答复进行情感分类处理。
步骤208，将问题对应的问题答复输入至问题对应的子模型，通过子模型运算，输出问题答复对应的情感分值。
服务器将待分类文本中问题对应的问题答复输入至问题对应的子模型中，调用子模型对问题答复进行情感分类。通过子模型的运算，输出该问题答复对应的情感分值。情感分值可以反映针对问题的问题答复的情感倾向。具体的，服务器通过调用子模型进行运算，得到问题答复对应的各个情感标签的概率。根据对应的情感标签的概率进行计算，得到问题答复对应的情感分值，输出该问题答复对应的情感分值。比如，利用Fasttext模型对待分类文本进行情感分类。服务器将待分类文本中的问题答复分别输入问题对应的Fasttext模型的子模型中，子模型对问题答复进行情感分类处理，得到多个情感标签对应的概率。例如情感标签可以包括积极和消极，该问题答复的情感分类结果为：积极概率68％，消极概率32％。根据问题答复的情感标签概率，计算出对应问题答复对应 的情感分值，比如满分值10分时，情感分值为7分。
步骤210，根据多个问题答复对应的情感分值识别待分类文本对应的情感类型。
服务器综合多个问题答复对应的情感分值，根据多个问题答复对应的情感分值识别该待分类文本对应的情感类型，完成对待分类文本进行情感分类的情感分类任务。
在本实施例中，在获取到情感分类任务中的待分类文本标识后，根据待分类文本标识获取对应待分类文本中的多个问题以及每个问题对应的问题答复，调用情感分类模型中的多个子模型，对每个问题对应的问题答复逐一进行情感分类，根据得到的每个问题答复对应的情感分值识别待分类文本对应的情感类型。通过调用对应的子模型，针对每一个问题的具体情况，对问题答复逐一进行情感分类，再综合每个问题答复对应的情感分值识别待分类文本对应的情感类型。与传统方式中对所有文本统一进行情感分类相比，充分结合问题，对相应的问题答复进行情感分类，有效的提高了对文本进行情感分类的准确性。
在其中一个实施例中，根据多个问题答复对应的情感分值识别待分类文本对应的情感类型，包括：获取每个问题对应的权重；根据权重和问题答复对应的情感分值进行计算，得到修改后的分值；将多个问题答复对应的修改后的分值进行累加，得到情感总分值；根据情感总分值识别待分类文本对应的情感类型。
服务器获取每个问题所对应的权重。由于每个问题对待分类文本的分类程度的重要性不同，为了均衡每个问题对情感分类程度的影响，每个问题都对应设置一个权重。用户可针对每个问题预先设置对应的重要性，服务器根据每个问题在所有问题中所占的重要性设置每个问题对应的权重。重要的问题所占的权重就可以高一些，简单的问题所占的权重就可以低一些。服务器根据权重和每个问题答复对应的情感分值进行计算，得到每个问题答复对应的修改后的分值。该修改后的分值可以客观体现对应的问题答复在待分类文本中的情感。例如，在面试过程中，可能会出现不同类型的问题，比如可以包括关于身份的问题、关于个人技能的问题和关于求职意向的问题等。在上述三类问题中，关于 身份的问题所占的权重可以比关于个人技能和求职意向的问题所占的权重轻一些。因此，根据三类问题的问题答复的情感分值，需要结合问题对应的权重，重新进行计算，以得到问题答复对应的修改后的分值。
服务器将计算得到的多个问题答复对应的修改后的分值进行累加，得到情感总分值。该情感总分值是综合了问题的待分类文本的情感总分值。服务器根据该情感总分值，识别待分类文本对应的情感类型，完成对待分类文本进行文本分类的情感分类任务。
在本实施例中，服务器通过获取每个问题对应的权重，计算每个问题答复所对应修改后的分值，累加多个问题答复对应的修改后的分值，得到待分类文本对应的情感总分值，通过情感总分值识别待分类文本对应的情感类型。通过结合每个问题对应的权重，解决了问题的重要性对最终情感类型的影响，有效的提高了对待分类文本进行情感分类的准确性。
在其中一个实施例中，在获取情感分类任务之前，上述基于情感的文本分类处理方法还包括：获取问题对应的答复数据；识别答复数据所对应的数据类型；当数据类型为语音类型时，将语音类型的答复数据转换为文本类型的答复数据；根据文本类型的答复数据以及问题生成待分类文本。
用户针对问题做出的答复数据可以包括多种数据类型。比如，可以包括文本类型和语音类型。服务器可以获取用户针对问题上传的答复数据。在获取到问题对应的答复数据后，服务器可以判断答复数据的数据类型。若数据类型是文本类型，则直接将文本类型的答复数据作为问题答复，结合多个问题和问题各自对应的问题答复生成待分类文本。若答复数据是语音类型的数据，服务器可以将语音数据转换为文本类型的答复数据，将该答复数据作为问题答复，生成待分类文本。
在本实施例中，除了接收文本类型的答复数据，服务器还可以接收语音类型的答复数据。通过将语音类型的答复数据转换为文本类型的答复数据，根据问题和文本类型的答复数据生成待分类文本，丰富了兼容的问题答复的数据类型，便于用户采用多种方式做出问题答复。
在其中一个实施例中，将问题以及对应的问题答复输入至对应的子模型， 通过子模型运算，输出问题对应的情感分值，包括：将问题答复进行分词，得到多个词语；根据问题调用对应的子模型；利用子模型对词语进行情感分类，得到多个情感表情对应的概率值；根据概率值确定问题答复对应的情感分值。
服务器可以采用多种方式对待分类文本中的问题答复进行分词，得到多个词语。具体的，服务器可以采用字符串匹配、理解、统计等方式中的一种或多种方式的结合对问题答复进行分词。字符串匹配方式是指将问题答复与预设词典中的词条进行匹配，若在词典中找到该字符串，则认为匹配成功，即识别出一个词。字符串匹配的方式可以包括正向字符串匹配和逆向字符串匹配。理解方式是指服务器在对问题答复进行词语切割时进行语义分析和句法分析，利用语义信息和句法信息处理切割词语时出现的歧义情况。统计方式是指服务器对问题答复中相邻共现的字的组合的频度进行统计，根据他们的共现信息进行分词。在其中一个实施例中，服务器在分词时还可以引入N-gram向量。通过得到的向量特征确定对问题答复进行分词后的词语顺序，更加准确的体现问题答复的内容，有效的提高了对待分类文本情感分类的准确性。
每个问题对应一个子模型。服务器获取问题与子模型之间的映射关系，根据映射关系调用问题对应的子模型。服务器将分词得到的多个词语输入子模型中，利用子模型对问题答复对应的词语进行情感分类，得到预设的多个情感标签分别对应的概率值。概率值表示该问题答复的情感属于对应情感标签的概率大小。服务器可以根据多个情感标签各自对应的概率值计算问题答复所对应的情感分值。
在本实施例中，通过对问题答复进行分词，调用问题对应的子模型对得到的词语进行情感分类，得到了问题答复对应的情感分值。针对每一个问题的具体内容调用对应的子模型，计算问题答复对应的情感分值，有效的提高了对待分类文本进行情感分类的准确性。
在其中一个实施例中，服务器在获取到待分类文本后，对待分类文本进行清理。比如删除不符合预设规则的待分类文本。再对清理之后的待分类文本进行情感分类。具体的，服务器将问题答复进行分词，得到多个词语后，根据问题调用对应的子模型。按照子模型的需要对多个词语进行整理，整理成子模型 需要的数据形式。比如，服务器可以将词语整理成表格形式，再将表格形式的词语输入子模型中，利用子模型对问题答复进行情感分类，得到问题答复对应的情感分值。
在本实施例中，获取到待分类文本后，对待分类文本进行清理，剔除不合法的待分类文本，对分词得到的词语进行整理，得到符合子模型对应的数据形式，输入子模型中。有效的提高了对待分类文本的情感分类效率。
在其中一个实施例中，在根据多个问题答复对应的情感分值识别待分类文本对应的情感类型之后，上述方法还包括：根据情感类型提取主体信息的步骤。如图3所示，该步骤具体包括：
步骤302，获取预设的情感需求类型。
步骤304，将分类后的情感类型与情感需求类型进行匹配，得到对应的第一匹配度。
步骤306，根据第一匹配度筛选出符合预设条件的目标情感类型。
步骤308，提取目标情感类型对应的主体信息。
服务器可以获取用户预设的情感需求类型，情感需求类型是指符合用户需求的情感类型，可以根据用户的实际需求预先设置。服务器可以采用多种方式将对待分类文本进行情感分类后得到的情感类型与情感需求类型进行匹配。具体的，服务器可以将分类后得到的情感类型逐一与情感需求类型进行匹配，还可以调用多个线程并行匹配情感类型与情感需求类型。服务器将情感类型与情感需求类型进行匹配后，得到该情感类型对应的第一匹配度。服务器对多个情感类型对应的第一匹配度进行筛选，筛选出符合预设条件的目标情感类型。预设条件可以是由用户预先设置的条件，比如筛选出第一匹配度大于阈值的情感类型。服务器提取符合预设条件的目标情感类型对应的主体信息，以得到符合情感需求类型的主体信息。
例如，在智能面试中，服务器将面试者提供的待分类文本进行情感分类后得到该面试者对应的情感类型，服务器可以获取招聘岗位对应的情感需求类型，将面试者们的情感类型与岗位的情感需求类型进行匹配，得到多个面试者们与该岗位之间的第一匹配度。服务器根据第一匹配度进行筛选，筛选出第一匹配 度符合预设条件的目标情感类型，该目标情感类型即是符合岗位情感需求类型的。服务器提取符合条件的目标情感类型对应的主体信息，得到符合岗位条件的主体信息。根据多个面试者的情感类型筛选出与岗位情感需求类型相匹配的目标情感类型，从多个面试者中根据情感类型初步筛选出符合招聘岗位需求的面试者，有效的提高了面试效率和准确性。
在本实施例中，通过将情感类型与情感需求类型进行匹配，根据第一匹配度筛选出符合预设条件的目标情感类型，有效的利用情感类型进行筛选，得到符合条件的主体信息，有效的丰富了目标筛选方式，提高了筛选效率。
在其中一个实施例中，在根据多个问题答复对应的情感分值识别待分类文本对应的情感类型之后，上述方法还包括：获取多种产品信息，产品信息包括产品类型；将产品类型与待分类文本对应的情感类型进行匹配，得到第二匹配度；当第二匹配度大于预定值时，将产品类型对应的产品信息标记为目标产品信息；提取待分类文本对应的终端标识，将目标产品信息推送至终端标识对应的终端。
服务器获取多种产品对应的产品信息，产品信息中包括了产品对应的产品类型。多种产品中可以包括不同产品类型的产品。例如，在基金类产品中，包括高风险高收益型产品，也包括低风险低收益型产品。服务器可以采用多种方式将多种产品的产品类型与待分类文本对应的情感类型进行匹配。具体的，服务器可以将多种产品对应的产品类型依次与情感类型进行匹配，也可以调用多个线程并行对产品类型与情感类型进行匹配，得到产品类型与情感类型之间的第二匹配度。当第二匹配度大于预定值时，将产品类型对应的产品信息标记为目标产品信息。预定值是由用户预先设定的值。当第二匹配度大于预定值时，则表示该产品类型对应的产品与该情感类型对应的用户是相匹配的。例如，高风险高收益型产品与偏向冒险型的用户比较匹配，低风险低收益型产品与偏向保守型的用户比较匹配。服务器提取上传待分类文本的终端所对应的终端标识，将该目标产品信息推送至终端标识对应的终端，完成符合情感类型匹配的产品信息推送。
在本实施例中，通过将产品类型与用户的情感类型进行匹配，为用户推送 符合情感类型的目标产品信息，有效的提高了对应信息推送的准确性。
可以理解的是，在其中一个实施例中，在获取多种产品信息，将产品类别与情感类型进行匹配之前，还可以获取情感需求类型，将分类后的情感类型与情感需求类型进行匹配，得到对应的第一匹配度，根据第一匹配度筛选出符合预设条件的情感类型。
服务器再将多种产品类型与筛选出的情感类型进行匹配，得到对应的第二匹配度，将第二匹配度大于预定值的产品类型对应的产品信息标记为目标产品信息，将目标产品信息推送至情感类型对应的终端。服务器可以利用情感类型对用户进行筛选，筛选出符合情感需求类型的用户。例如，服务器可以根据情感类型筛选出对购买产品有积极意向的用户，再将产品类型与筛选出的用户的情感类型进行匹配，从多种产品类型中筛选出符合用户情感类型的产品类型。到达给有购买意向的用户推送符合用户情感类型的产品信息的效果。
在本实施例中，将对情感类型进行筛选和对产品类型进行筛选相结合，有效的提高了产品信息推送的效率和准确性。
在其中一个实施例中，上述方法还包括：建立通用情感分类模型，通用情感分类模型包括通用子模型；获取训练数据集，训练数据集中包括多个训练文本和训练文本对应的标准情感分值；调用通用子模型对训练文本进行运算，得到训练情感分值；将训练情感分值与标准情感分值进行比对；根据比对结果对通用子模型进行调整，得到目标子模型。
服务器可以建立通用情感分类模型，通用情感分类模型可以采用Fasttext模型。服务器接收训练数据集，训练数据集中可以包括多个训练文本，以及训练文本对应的标准情感分值。训练文本中包括标准问题、标准问题对应的问题答复。每个标准问题可以对应多个问题答复，和多个问题答复各自对应的标准情感分值。服务器可以利用训练数据集对通用情感分类模型进行训练。具体的，服务器调用标准问题对应的通用情感分类模型中的通用子模型，将对应的问题答复输入通用子模型中，通过通用子模型运算，输出训练情感分值。服务器可以将训练情感分值与标准情感分值进行比对，根据比对结果对相应的通用子模型进行调整。调整后重复将问题答复输入对应的通用子模型中，对通用子模型 进行调整，直到训练情感分值与标准情感分值比对成功，得到标准问题对应的目标子模型。服务器利用多个训练文本中的标准问题、问题答复以及标准情感分值对通用情感分类模型的多个通用子模型进行训练，得到目标情感分类模型。
在本实施例中，服务器利用训练数据集对通用情感分类模型进行训练，得到包括多个目标子模型的目标情感分类模型，有效的提高了目标情感分类模型基于情感对待分类文本进行分类的准确度。
应该理解的是，虽然图2-3的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，这些步骤可以以其它的顺序执行。而且，图2-3中的至少一部分步骤可以包括多个子步骤或者多个阶段，这些子步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，这些子步骤或者阶段的执行顺序也不必然是依次进行，而是可以与其它步骤或者其它步骤的子步骤或者阶段的至少一部分轮流或者交替地执行。
在其中一个实施例中，如图4所示，提供了一种基于情感的文本分类处理装置，包括：任务获取模块402、文本获取模块404、模型调用模块406和情感类型识别模块408，其中：
任务获取模块402，用于获取情感分类任务，情感分类任务中包括待分类文本标识。
文本获取模块404，用于根据待分类文本标识获取对应的待分类文本，待分类文本包括多个问题以及问题对应的问题答复。
模型调用模块406，用于根据情感分类任务调用对应的情感分类模型，情感分类模型包括多个与问题对应的子模型；将问题对应的问题答复输入至问题对应的子模型，通过子模型运算，输出问题答复对应的情感分值。
情感类型识别模块408，用于根据多个问题答复对应的情感分值识别待分类文本对应的情感类型。
在其中一个实施例中，上述模型调用模块406还用于将问题答复进行分词，得到多个词语；根据问题调用对应的子模型；利用子模型对词语进行情感分类，得到多个情感标签对应的概率值；根据概率值确定问题答复对应的情感分值。
在其中一个实施例中，上述装置还包括需求类型匹配模块，用于获取预设的情感需求类型；将分类后的情感类型与情感需求类型进行匹配，得到情感类型对应的第一匹配度；根据第一匹配度筛选出符合预设条件的目标情感类型；提取目标情感类型对应的主体信息。
在其中一个实施例中，上述装置还包括产品推送模块，用于获取多种产品信息，产品信息包括产品类型；将产品类型与待分类文本对应的情感类型进行匹配，得到第二匹配度；当第二匹配度大于预定值时，将产品类型对应的产品信息标记为目标产品信息；提取待分类文本对应的终端标识，将目标产品信息推送至终端标识对应的终端。
在其中一个实施例中，上述情感类型识别模块408还用于获取每个问题对应的权重；根据权重和问题答复对应的情感分值进行计算，得到修改后的分值；将多个问题答复对应的修改后的分值进行累加，得到情感总分值；根据情感总分值识别待分类文本对应的情感类型。
在其中一个实施例中，上述任务获取模块402还用于获取问题对应的答复数据；识别答复数据所对应的数据类型；当数据类型为语音类型时，将语音类型的答复数据转换为文本类型的答复数据；根据文本类型的答复数据以及问题生成待分类文本。
在其中一个实施例中，上述装置还包括模型训练模块，用于建立通用情感分类模型，通用情感分类模型包括通用子模型；获取训练数据集，训练数据集中包括多个训练文本和训练文本对应的标准情感分值；调用通用子模型对训练文本进行运算，得到训练情感分值；将训练情感分值与标准情感分值进行比对；根据比对结果对通用子模型进行调整，得到目标子模型。
关于基于情感的文本分类处理装置的具体限定可以参见上文中对于基于情感的文本分类处理方法的限定，在此不再赘述。上述基于情感的文本分类处理装置中的各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内嵌于或独立于计算机设备中的处理器中，也可以软件形式存储于计算机设备中的存储器中，以便于处理器调用执行以上各个模块对应的操作。
在一个实施例中，提供了一种计算机设备，该计算机设备可以是服务器， 其内部结构图可以如图5所示。该计算机设备包括通过***总线连接的处理器、存储器、网络接口和数据库。其中，该计算机设备的处理器用于提供计算和控制能力。该计算机设备的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有操作***、计算机可读指令和数据库。该内存储器为非易失性存储介质中的操作***和计算机可读指令的运行提供环境。该计算机设备的数据库用于存储基于情感的文本分类处理数据。该计算机设备的网络接口用于与外部的终端通过网络连接通信。该计算机可读指令被处理器执行时以实现一种基于情感的文本分类处理方法。
本领域技术人员可以理解，图5中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的计算机设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
一种计算机设备，包括存储器和一个或多个处理器，存储器中储存有计算机可读指令，计算机可读指令被处理器执行时，使得一个或多个处理器执行时实现上述方法实施例中的步骤。
一个或多个存储有计算机可读指令的非易失性计算机可读存储介质，计算机可读指令被一个或多个处理器执行时，使得一个或多个处理器执行时实现上述方法实施例中的步骤。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机可读指令来指令相关的硬件来完成，的计算机可读指令可存储于一非易失性计算机可读取存储介质中，该计算机可读指令在执行时，可包括如上述各方法的实施例的流程。其中，本申请所提供的各实施例中所使用的对存储器、存储、数据库或其它介质的任何引用，均可包括非易失性和/或易失性存储器。非易失性存储器可包括只读存储器(ROM)、可编程ROM(PROM)、电可编程ROM(EPROM)、电可擦除可编程ROM(EEPROM)或闪存。易失性存储器可包括随机存取存储器(RAM)或者外部高速缓冲存储器。作为说明而非局限，RAM以多种形式可得，诸如静态RAM(SRAM)、动态RAM(DRAM)、同步DRAM(SDRAM)、双数据率SDRAM(DDRSDRAM)、增强型SDRAM (ESDRAM)、同步链路(Synchlink)DRAM(SLDRAM)、存储器总线(Rambus)直接RAM(RDRAM)、直接存储器总线动态RAM(DRDRAM)、以及存储器总线动态RAM(RDRAM)等。
以上实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
以上所述实施例仅表达了本申请的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出若干变形和改进，这些都属于本申请的保护范围。因此，本申请专利的保护范围应以所附权利要求为准。

Claims (20)

  1. 一种基于情感的文本分类处理方法，包括：
    获取情感分类任务，所述情感分类任务中包括待分类文本标识；
    根据所述待分类文本标识获取对应的待分类文本，所述待分类文本包括多个问题以及所述问题对应的问题答复；
    根据所述情感分类任务调用对应的情感分类模型，所述情感分类模型包括多个与所述问题对应的子模型；
    将所述问题对应的问题答复输入至所述问题对应的子模型，通过子模型运算，输出所述问题答复对应的情感分值；及
    根据多个问题答复对应的情感分值识别所述待分类文本对应的情感类型。
  2. 根据权利要求1所述的方法，其特征在于，所述将所述问题以及对应的问题答复输入至对应的子模型，通过子模型运算，输出所述问题对应的情感分值，包括：
    将所述问题答复进行分词，得到多个词语；
    根据所述问题调用对应的子模型；
    利用所述子模型对所述词语进行情感分类，得到多个情感标签对应的概率值；及
    根据所述概率值确定所述问题答复对应的情感分值。
  3. 根据权利要求1所述的方法，其特征在于，在所述根据多个问题对应的情感分值识别所述待分类文本对应的情感类型之后，所述方法还包括：
    获取预设的情感需求类型；
    将分类后的情感类型与所述情感需求类型进行匹配，得到所述情感类型对应的第一匹配度；
    根据所述第一匹配度筛选出符合预设条件的目标情感类型；及
    提取所述目标情感类型对应的主体信息。
  4. 根据权利要求1-3中任一项所述的方法，其特征在于，在所述根据多个问题答复对应的情感分值识别所述待分类文本对应的情感类型之后，所述方法还包括：
    获取多种产品信息，所述产品信息包括产品类型；
    将所述产品类型与所述待分类文本对应的情感类型进行匹配，得到第二匹配度；
    当所述第二匹配度大于预定值时，将所述产品类型对应的产品信息标记为目标产品信息；及
    提取所述待分类文本对应的终端标识，将所述目标产品信息推送至所述终端标识对应的终端。
  5. 根据权利要求1所述的方法，其特征在于，所述根据多个问题答复对应的情感分值识别所述待分类文本对应的情感类型，包括：
    获取每个问题对应的权重；
    根据所述权重和所述问题答复对应的情感分值进行计算，得到修改后的分值；
    将多个问题答复对应的修改后的分值进行累加，得到情感总分值；及
    根据所述情感总分值识别所述待分类文本对应的情感类型。
  6. 根据权利要求1所述的方法，其特征在于，在所述获取情感分类任务之前，所述方法还包括：
    获取所述问题对应的答复数据；
    识别所述答复数据所对应的数据类型；
    当所述数据类型为语音类型时，将所述语音类型的答复数据转换为文本类型的答复数据；及
    根据所述文本类型的答复数据以及所述问题生成待分类文本。
  7. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    建立通用情感分类模型，所述通用情感分类模型包括通用子模型；
    获取训练数据集，所述训练数据集中包括多个训练文本和训练文本对应的标准情感分值；
    调用所述通用子模型对所述训练文本进行运算，得到训练情感分值；
    将所述训练情感分值与所述标准情感分值进行比对；及
    根据比对结果对所述通用子模型进行调整，得到目标子模型。
  8. 一种基于情感的文本分类处理装置，所述装置包括：
    任务获取模块，用于获取情感分类任务，所述情感分类任务中包括待分类文本标识；
    文本获取模块，用于根据所述待分类文本标识获取对应的待分类文本，所述待分类文本包括多个问题以及所述问题对应的问题答复；
    模型调用模块，用于根据所述情感分类任务调用对应的情感分类模型，所述情感分类模型包括多个与所述问题对应的子模型；将所述问题对应的问题答复输入至所述问题对应的子模型，通过子模型运算，输出所述问题答复对应的情感分值；及
    情感类型识别模块，用于根据多个问题答复对应的情感分值识别所述待分类文本对应的情感类型。
  9. 根据权利要求8所述的装置，其特征在于，所述模型调用模块还用于将所述问题答复进行分词，得到多个词语；根据所述问题调用对应的子模型；利用所述子模型对所述词语进行情感分类，得到多个情感标签对应的概率值；根据所述概率值确定所述问题答复对应的情感分值。
  10. 一种计算机设备，包括存储器及一个或多个处理器，所述存储器存储有至少一条计算机可读指令，所述计算机可读指令被所述一个或多个处理器执行时，使得所述一个或多个处理器加载并执行以下步骤：
    获取情感分类任务，所述情感分类任务中包括待分类文本标识；
    根据所述待分类文本标识获取对应的待分类文本，所述待分类文本包括多个问题以及所述问题对应的问题答复；
    根据所述情感分类任务调用对应的情感分类模型，所述情感分类模型包括多个与所述问题对应的子模型；
    将所述问题对应的问题答复输入至所述问题对应的子模型，通过子模型运算，输出所述问题答复对应的情感分值；及
    根据多个问题答复对应的情感分值识别所述待分类文本对应的情感类型。
  11. 根据权利要求10所述的计算机设备，其特征在于，所述处理器执行计算机可读指令时还执行以下步骤：将所述问题答复进行分词，得到多个词语； 根据所述问题调用对应的子模型；利用所述子模型对所述词语进行情感分类，得到多个情感标签对应的概率值；及根据所述概率值确定所述问题答复对应的情感分值。
  12. 根据权利要求10所述的计算机设备，其特征在于，所述处理器执行计算机可读指令时还执行以下步骤：获取预设的情感需求类型；将分类后的情感类型与所述情感需求类型进行匹配，得到所述情感类型对应的第一匹配度；根据所述第一匹配度筛选出符合预设条件的目标情感类型；及提取所述目标情感类型对应的主体信息。
  13. 根据权利要求10-12中任一项所述的计算机设备，其特征在于，所述处理器执行计算机可读指令时还执行以下步骤：获取多种产品信息，所述产品信息包括产品类型；将所述产品类型与所述待分类文本对应的情感类型进行匹配，得到第二匹配度；当所述第二匹配度大于预定值时，将所述产品类型对应的产品信息标记为目标产品信息；及提取所述待分类文本对应的终端标识，将所述目标产品信息推送至所述终端标识对应的终端。
  14. 根据权利要求10所述的计算机设备，其特征在于，所述处理器执行计算机可读指令时还执行以下步骤：获取每个问题对应的权重；根据所述权重和所述问题答复对应的情感分值进行计算，得到修改后的分值；将多个问题答复对应的修改后的分值进行累加，得到情感总分值；及根据所述情感总分值识别所述待分类文本对应的情感类型。
  15. 根据权利要求10所述的计算机设备，其特征在于，所述处理器执行计算机可读指令时还执行以下步骤：获取所述问题对应的答复数据；识别所述答复数据所对应的数据类型；当所述数据类型为语音类型时，将所述语音类型的答复数据转换为文本类型的答复数据；及根据所述文本类型的答复数据以及所述问题生成待分类文本。
  16. 一个或多个存储有计算机可读指令的非易失性计算机可读存储介质，所述计算机可读指令被一个或多个处理器执行时，使得所述一个或多个处理器执行以下步骤：
    获取情感分类任务，所述情感分类任务中包括待分类文本标识；
    根据所述待分类文本标识获取对应的待分类文本，所述待分类文本包括多个问题以及所述问题对应的问题答复；
    根据所述情感分类任务调用对应的情感分类模型，所述情感分类模型包括多个与所述问题对应的子模型；
    将所述问题对应的问题答复输入至所述问题对应的子模型，通过子模型运算，输出所述问题答复对应的情感分值；及
    根据多个问题答复对应的情感分值识别所述待分类文本对应的情感类型。
  17. 根据权利要求16所述的存储介质，其特征在于，所述计算机可读指令被所述处理器执行时还执行以下步骤：将所述问题答复进行分词，得到多个词语；根据所述问题调用对应的子模型；利用所述子模型对所述词语进行情感分类，得到多个情感标签对应的概率值；及根据所述概率值确定所述问题答复对应的情感分值。
  18. 根据权利要求16所述的存储介质，其特征在于，所述计算机可读指令被所述处理器执行时还执行以下步骤：获取预设的情感需求类型；将分类后的情感类型与所述情感需求类型进行匹配，得到所述情感类型对应的第一匹配度；根据所述第一匹配度筛选出符合预设条件的目标情感类型；及提取所述目标情感类型对应的主体信息。
  19. 根据权利要求16-18中任一项所述的存储介质，其特征在于，所述计算机可读指令被所述处理器执行时还执行以下步骤：获取多种产品信息，所述产品信息包括产品类型；将所述产品类型与所述待分类文本对应的情感类型进行匹配，得到第二匹配度；当所述第二匹配度大于预定值时，将所述产品类型对应的产品信息标记为目标产品信息；及提取所述待分类文本对应的终端标识，将所述目标产品信息推送至所述终端标识对应的终端。
  20. 根据权利要求16所述的存储介质，其特征在于，所述计算机可读指令被所述处理器执行时还执行以下步骤：获取每个问题对应的权重；根据所述权重和所述问题答复对应的情感分值进行计算，得到修改后的分值；将多个问题答复对应的修改后的分值进行累加，得到情感总分值；及根据所述情感总分值识别所述待分类文本对应的情感类型。
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