WO2020082734A1 - Procédé et appareil de reconnaissance d'émotions dans un texte, dispositif électronique, et support d'enregistrement non volatil lisible par ordinateur - Google Patents

Procédé et appareil de reconnaissance d'émotions dans un texte, dispositif électronique, et support d'enregistrement non volatil lisible par ordinateur Download PDF

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WO2020082734A1
WO2020082734A1 PCT/CN2019/089166 CN2019089166W WO2020082734A1 WO 2020082734 A1 WO2020082734 A1 WO 2020082734A1 CN 2019089166 W CN2019089166 W CN 2019089166W WO 2020082734 A1 WO2020082734 A1 WO 2020082734A1
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text
sample
emotion
cost
error rate
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PCT/CN2019/089166
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English (en)
Chinese (zh)
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方豪
马骏
王少军
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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
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/35—Clustering; Classification

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  • the present application relates to the field of artificial intelligence technology, and in particular, to a text emotion recognition method, device, electronic device, and computer non-volatile readable storage medium.
  • emotional recognition of text is an important task, such as emotional recognition of service evaluations made by users, emotional recognition and classification of Internet articles, etc., so as to better understand user demands or achieve precise positioning of text With beneficial effects such as recommendation.
  • an object of the present application is to provide a text emotion recognition method, device, electronic device, and computer non-volatile readable storage medium.
  • a text sentiment recognition method is characterized by comprising: acquiring a sample text set, the sample text set includes a plurality of sample texts and an emotion classification label corresponding to each of the sample texts; according to the The number distribution of sentiment classification labels corrects the initial cost to obtain a modified cost; through the sample text set and the modified cost, a lifting algorithm learning model is trained to obtain a text emotion recognition model; the text emotion recognition model is used to recognize Recognize the text to obtain the emotion recognition result of the text to be recognized.
  • a text sentiment recognition device is characterized by comprising: a sample acquisition module for acquiring a sample text set, the sample text set including a plurality of sample texts and emotion classification tags corresponding to each of the sample texts;
  • the cost correction module is used for correcting and calculating the initial cost according to the number distribution of the sentiment classification tags in the sample text set, to obtain the correction cost.
  • the model acquisition module is used to train a lifting algorithm learning model through the sample text set and the correction cost to obtain a text emotion recognition model.
  • the target recognition module is used for recognizing the text to be recognized through the text emotion recognition model to obtain the emotion recognition result of the text to be recognized.
  • a text emotion recognition device includes a processor and a memory, and the memory stores computer-readable instructions, which are implemented by the processor to implement the text emotion recognition method as described above .
  • a computer non-volatile readable storage medium has stored thereon a computer program, which when executed by a processor implements the text emotion recognition method as described above.
  • a text emotion recognition model is trained and obtained based on the acquired sample text set and the modified cost weights obtained based on the number distribution of different emotion sample texts, and then emotion recognition is performed on the text to be recognized through the text emotion recognition model.
  • the initial cost is corrected according to the number distribution of sample texts of different emotions, so that the correction cost can balance the deviation of the number of sample texts of different emotions, which can improve the accuracy and balance of the recognition rate of different emotion texts by the text emotion recognition model.
  • FIG. 1 schematically shows a flowchart of a text emotion recognition method in this exemplary embodiment
  • FIG. 2 schematically shows a sub-flow diagram of a text emotion recognition method in this exemplary embodiment
  • FIG. 3 schematically shows a sub-flow diagram of another text emotion recognition method in this exemplary embodiment
  • FIG. 4 schematically shows a structural block diagram of a text emotion recognition device in this exemplary embodiment
  • FIG. 5 shows a block diagram of an electronic device for implementing the above method according to an exemplary embodiment
  • FIG. 6 shows a schematic diagram of a computer non-volatile readable storage medium for implementing the above method according to an exemplary embodiment.
  • Example embodiments will now be described more fully with reference to the drawings.
  • the example embodiments can be implemented in various forms and should not be construed as being limited to the examples set forth herein; on the contrary, providing these embodiments makes the disclosure more comprehensive and complete, and fully conveys the concept of the example embodiments For those skilled in the art.
  • the described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner.
  • Exemplary embodiments of the present disclosure first provide a text emotion recognition method, where text generally refers to information in text form.
  • speech information can also be converted into text by a specific tool for emotion recognition; emotion recognition may be Classify and judge the sentiment states conveyed by the text, such as whether the sentiment of the text is positive or negative, commendatory or derogatory, etc.
  • the text emotion recognition method may include the following steps S110-S140:
  • Step S110 Acquire a sample text set, where the sample text set includes a plurality of sample texts and emotion classification tags corresponding to the sample texts.
  • the sample text may be text extracted from the corpus of a specific application scenario, and may generally cover various types of text in the corpus. According to the needs of text emotion recognition in this application scenario, the sample text can be labeled with emotion classification to obtain the emotion classification label.
  • emotion classification For example, in the scenario of identifying the emotions of e-commerce consumers on product evaluation, it is usually necessary to classify emotions as positive and negative , You can extract a large number of sample texts from the evaluation text and mark them as positive or negative emotional texts one by one; for example, when identifying the personal dynamic emotions of social network users, you usually need to classify the emotions as "happy” and "frustrated” "," Anger ",” sadness “and other categories, for the sample text” weather is too good ", you can label its emotion classification label as” happy ", for the sample text” really bad luck today ", you can label its emotion classification label For "frustrated” etc.
  • the specific content of the emotion classification label is not particularly limited.
  • step S120 the initial cost is corrected and calculated according to the number distribution of the sentiment classification tags in the sample text set to obtain the modified cost.
  • cost is a concept in cost-sensitive learning, reflecting the severity of consequences caused by misidentification.
  • the initial cost may be a parameter determined from the application scenario and considering the cost of misrecognizing the sentiment of the text.
  • the initial cost of incorrect recognition of texts of different emotion types is usually different; in different application scenarios, the initial cost of incorrect recognition of text of the same emotion type may also be different. For example, when using a positive evaluation system to evaluate agent customer service personnel, they generally pay more attention to the positive emotional evaluations given by customers to encourage and praise excellent customer service personnel.
  • the positive emotional text is mistakenly identified as the initial negative emotional text
  • the cost is high, and the initial cost of misrecognizing negative emotional text as positive emotional text is low; when evaluating e-commerce products, usually pay more attention to the negative emotional evaluation given by consumers to improve product quality.
  • the initial cost of misidentifying negative emotional text as positive emotional text is higher, and the initial cost of misidentifying positive emotional text as negative emotional text is lower.
  • the distribution of the number of sentiment classification labels reflects the imbalance of the sample texts of different emotions, which can be quantitatively expressed by one or more indicators such as the ratio, variance, or standard deviation between the sample texts of different emotions, for example:
  • the number distribution of sentiment classification tags in the sample set can be 4: 1; or in the sample text set, the number The distribution reflects that the "positive" sentiment classification tags account for 4/5 of the total sentiment classification tags, and the "negative” sentiment classification tags account for 1/5 of the total sentiment classification tags.
  • variance or standard deviation is usually used to represent the number distribution of sentiment classification tags. This embodiment is not particularly limited.
  • the initial cost of texts of different sentiment types can be corrected and calculated through a specific function or formula, and combined with the desired correction direction, the correction cost can be obtained. For example, if the proportion of positive sample text is low or the number is small, the initial cost of positive emotional text can be modified to have a higher cost weight. If the proportion of negative sample text is low or the number is small, the initial cost of negative emotional text can be modified to make it have a higher cost weight.
  • Step S130 Train a lifting algorithm learning model through the sample text set and the correction cost to obtain a text emotion recognition model.
  • the lifting algorithm learning model can be applied to the scenario of improving the accuracy of the weak classification algorithm.
  • the lifting algorithm learning model can set different sampling weights for sample texts with different accuracy rates, so that the model pays more attention to the correction cost. High sample text.
  • the lifting algorithm learning model may include a variety of models, for example, gradient lifting decision tree model, Adaboost model or Xgboost model.
  • the training process can include: lifting the algorithm learning model to take the sample text as input, output the sentiment classification result of the sample text, and compare the sentiment classification result with the sentiment classification label; and then calculate the comparison result by correcting the cost to obtain the accuracy of the model recognition Rate; by iteratively adjusting the parameters of the model until the accuracy rate reaches a certain standard, it can be considered that the training is complete.
  • the trained learning model of the lifting algorithm is the text emotion recognition model.
  • Step S140 Recognize the text to be recognized through the text emotion recognition model to obtain the emotion recognition result of the text to be recognized.
  • the text emotion recognition model completed through the above training can recognize the text to be recognized, and the emotion recognition result is the emotion classification result of the text to be recognized.
  • the emotion recognition result may be a positive emotion text or a negative emotion text.
  • a text emotion recognition model is trained and obtained, and then the recognized text is treated through the text emotion recognition model Perform emotion recognition.
  • the initial cost is corrected according to the number distribution of sample texts of different emotions, so that the correction cost can balance the deviation of the number of sample texts of different emotions, which can improve the accuracy and balance of the recognition rate of different emotion texts by the text emotion recognition model.
  • the sentiment classification label may include positive sentiment text and negative sentiment text.
  • Step S120 can be implemented by the following steps:
  • Cost 10 is the initial cost of mistaken positive emotion text as negative emotion text
  • cost 01 is the initial cost of mistaken negative emotion text as positive emotion text.
  • the number of positive emotional text Q1 and the number of negative emotional text Q0 in the sample text set are counted.
  • R 10 is the sample deviation ratio
  • costm 10 is the correction cost of mistaken positive emotion text as negative emotion text
  • costm 01 is the correction cost of mistaken negative emotion text as positive emotion text
  • a is the exponential parameter.
  • sample texts with different sentiment classifications in the sample text set have different initial costs and correction costs.
  • emotion classification labels are positive emotion text and negative emotion text
  • "0" can be used to indicate negative emotions
  • "1” can be used to indicate positive emotions.
  • the obtained initial costs cost 10 and cost 01 can respectively represent the initial cost of misrepresenting positive emotional text as negative emotional text and the initial cost of misrepresenting negative emotional text as positive emotional text.
  • the correction cost can be calculated by formula (1), formula (2) and formula (3), a is the index parameter, reflecting the degree of correction, the greater a , Indicates the higher the degree of correction; generally 0 ⁇ a ⁇ 1, the value of a can be set according to experience and actual use.
  • the initial cost can also be corrected by calculating the deviation ratio of the sample texts of different sentiment classifications.
  • the number of negative emotion texts is Q0
  • the number of positive emotion texts is Q1
  • the deviation ratio of negative emotions can be:
  • the correction cost of positive emotional text can also be lower than its initial cost, and the correction cost of negative emotional text is higher than its initial cost.
  • step S130 may include the following steps:
  • step S202 the training subset T is used to train the lifting algorithm to learn the model.
  • step S203 the emotion recognition result f (xi) of each sample text xi in the verification subset D is obtained by improving the algorithm learning model.
  • Step S204 calculating the error rate of the algorithm learning model according to formula (4):
  • Step S205 if the error rate is lower than the learning threshold, it is determined that the training model of the lifting algorithm learning is completed, and the trained learning model of the lifting algorithm is determined as a text emotion recognition model.
  • m is the number of sample texts in the verification subset, i ⁇ [1, m]; E is the error rate of the algorithm learning model, D + is the positive emotion sample text subset of verification subset D, and D- is the verification subset D is the negative sentiment sample text subset, y i is the sentiment classification label of the sample text xi.
  • the lifting algorithm learning model can take the training subset as input, output the sentiment classification result of the sample text in the training subset, adjust the model parameters, continue training the model, and then can verify whether the model meets the requirements by verifying the subset, by formula (4)
  • the calculation improves the error rate of the algorithm learning model.
  • II ( ⁇ ) is the indicator function, and the values in brackets are 1 and 0 when true and false, respectively.
  • the error index of xi is 0; if the output of the model is different from the sentiment classification label, the error index of xi is costm10 (when xi is positive sample text) or costm01 (when xi is negative Sample text); taking the arithmetic average of the error indices of all sample texts in D, the error rate E of the model can be obtained. The lower the value of the error rate E, the better the effect of improving the algorithm learning model training.
  • a learning threshold judgment mechanism can be set to judge whether the error rate of the improved algorithm learning model is within an acceptable range. If the calculated error rate is lower than the learning threshold, it is judged that the model training is completed to obtain a text emotion recognition model; if the calculated error rate is equal to or higher than the learning threshold, it cannot pass the verification, and the model can continue to be trained.
  • the learning threshold can be set according to experience or actual usage, and this embodiment does not limit its specific value.
  • the text emotion recognition method may further include the following steps:
  • s is the number of positive sentiment sample texts in the verification subset D, that is, the number of sample texts in D +
  • v is the number of negative sentiment sample texts in the verification subset D, namely the number of sample texts in D-
  • m s + v.
  • the positive sample error rate E + and the negative sample error rate E- of the improved algorithm learning model can be calculated according to formulas (5) and (6), respectively.
  • the positive sample error rate E + is The positive sample text subset D + verification is used to increase the error rate of the algorithm learning model, that is, the error rate for positive sample text recognition;
  • the negative sample error rate E- is the negative sample text subset D- verification to improve the algorithm learning model error rate, That is, the error rate for negative sample text recognition.
  • the error rate calculated by the above formula (4) is the error rate for the overall recognition of positive sample text and negative sample text.
  • the formula can also be used.
  • the error rate of the sample text subset D verification lifting algorithm learning model is calculated, which is consistent with the error rate calculated by the above formula (4).
  • the error rate ratio A of the lifting algorithm model can be calculated.
  • A reflects the imbalance of the error rate of the model for different emotion sample text recognition.
  • A 1
  • the error rate of the model for the positive sample text and the negative sample text recognition is balanced; when A and 1 are too different, whether it is greater than 1 Or less than 1, it means that the model has a high degree of unevenness in the recognition rate of positive sample text and negative sample text, and the training has not met the requirements.
  • This embodiment means that before determining whether the error rate of the learning model of the lifting algorithm meets the requirement, first determine whether the error rate of the model identifying different emotion sample texts is balanced, and if the balance reaches the requirement, continue to determine whether the error rate meets the requirement.
  • a preset range can be set to measure whether the error rate balance meets the requirements.
  • the error rate ratio is within the preset range, it means that the balance reaches the requirements and can be continued Determine whether the error rate reaches the standard of learning threshold.
  • the calculated error rate ratio A 2 which is within the preset range, indicates that this degree of imbalance can be accepted, and continues to detect whether the error rate is below the learning threshold.
  • B
  • can also be used to quantitatively express the degree of unbalanced error rate of the lifting algorithm learning model for different emotion sample text recognition.
  • B 0, it means complete equilibrium. The larger the B, the more balanced it is. Poor, so you can set a threshold on B to measure whether the model's error rate balance meets the requirements.
  • the text emotion recognition method further includes the following steps:
  • the training subset T is used again to train the lifting algorithm to learn the model.
  • FIG. 3 shows a flow chart of a text emotion recognition model training in this exemplary embodiment.
  • the sample deviation ratio is calculated for the sample text set, and the correction cost is calculated according to the sample deviation ratio to train the lifting algorithm to learn the model; and then calculate The error rate ratio and error rate of model training, and judge accordingly; if it is judged that the error rate ratio is not within the preset range, you can return to the model training step to continue training to improve the algorithm to learn the model, if the error rate ratio is judged to be preset Within the range, you can continue to judge whether the error rate is lower than the learning threshold; further, if you judge that the error rate is equal to or higher than the learning threshold, you can return to the model training step to continue training to improve the algorithm to learn the model, if you judge the error rate is low Based on the learning threshold, it can be considered that the model training is completed and a text emotion recognition model is obtained.
  • the emotion classification tags may include: level 1 positive emotion text, level 2 positive emotion text, ..., level n positive emotion text and level 1 negative emotion text, level 2 negative emotion text, ..., N-level negative emotion text, n is an integer greater than 1.
  • the emotions of the sample text can be classified into positive emotions and negative emotions. Further, positive emotions and negative emotions can be divided into level 1 positive emotion text, level 2 positive emotion text, ..., level n positive emotion text and Level 1 negative emotion text, level 2 negative emotion text, ..., level n negative emotion text.
  • the sentiment classification tags may also include neutral sentiment text, etc., which is not specifically limited here.
  • the apparatus 400 may include a sample acquisition module 410, a cost correction module 420, a model acquisition module 430, and a target recognition module 440.
  • the sample acquisition module 410 is used to acquire a sample text set
  • the sample text set includes multiple sample texts and the sentiment classification tags corresponding to each sample text
  • the cost correction module 420 is used to determine the initial value based on the number of sentiment classification tags in the sample text set.
  • the cost is corrected and calculated to obtain the modified cost;
  • the model acquisition module 430 is used to train a lifting algorithm to learn the model through the sample text set and the modified cost, and the text emotion recognition model is obtained;
  • the target recognition module 440 is used to treat the recognized text through the text emotion recognition model Recognize and get the emotion recognition result of the text to be recognized.
  • the sentiment classification label includes positive sentiment text and negative sentiment text
  • the cost correction module may include: an initial cost acquisition unit, used to obtain initial costs cost 10 and cost 01 , cost 10 is the positive sentiment text error Think of the initial cost of negative emotion text, cost 01 is the initial cost of mistaken negative emotion text as positive emotion text; text statistics unit, used to count the number of positive emotion text Q1 and the number of negative emotion text Q0 in the sample text set; cost correction unit , Used to modify the initial cost by the following formula to obtain the modified cost:
  • R 10 is the sample deviation ratio
  • costm 10 is the correction cost of mistaken positive emotion text as negative emotion text
  • costm 01 is the correction cost of mistaken negative emotion text as positive emotion text
  • a is the exponential parameter.
  • Judgment unit used to determine that the lifting algorithm learning model training is completed when the error rate is lower than the learning threshold, and determine the trained lifting algorithm learning model as a text emotion recognition model;
  • m is the number of sample text in the verification subset, i ⁇ [1, m];
  • E is the error rate of the learning model of the algorithm,
  • D + is the positive emotion sample text subset of the verification subset D,
  • D- is the negative emotion sample text subset of the verification subset D, and
  • y i is the sample Sentiment classification label for text xi.
  • the calculation unit may also be used to calculate the positive sample error rate E + and the negative sample error rate E- of the boosting algorithm learning model according to equations (5) and (6), respectively:
  • the judgment unit can also be used to continue to detect whether the error rate is lower than the learning threshold when the error rate ratio is within a preset range.
  • s is the number of positive emotion sample texts in the verification subset D
  • v is the number of negative emotion sample texts in the verification subset D
  • m s + v.
  • the training unit can also be used to train the lifting algorithm to learn the model again using the training subset T if the error rate ratio is not within the preset range;
  • the computing unit can also be used to recalculate the lifting algorithm by the following formula The error rate ratio of the learning model:
  • the judgment unit can also be used to detect again whether the error rate ratio is within a preset range.
  • the emotion classification tags may include level 1 positive emotion text, level 2 positive emotion text, ..., level n positive emotion text and level 1 negative emotion text, level 2 negative emotion text, ..., n Grade negative emotion text, n is an integer greater than 1.
  • the lifting algorithm learning model may include a gradient lifting decision tree model, an Adaboost model, or an Xgboost model.
  • Exemplary embodiments of the present disclosure also provide an electronic device capable of implementing the above method.
  • the electronic device 500 according to this exemplary embodiment of the present disclosure is described below with reference to FIG. 5.
  • the electronic device 500 shown in FIG. 5 is only an example, and should not bring any limitation to the functions and use scope of the embodiments of the present disclosure.
  • the electronic device 500 is represented in the form of a general-purpose computing device.
  • the components of the electronic device 500 may include, but are not limited to: the at least one processing unit 510, the at least one storage unit 520, a bus 530 connecting different system components (including the storage unit 520 and the processing unit 510), and the display unit 540.
  • the storage unit stores the program code
  • the program code may be executed by the processing unit 510, so that the processing unit 510 executes the steps according to various exemplary embodiments of the present disclosure described in the "Exemplary Method" section of this specification.
  • the processing unit 510 may execute steps S110 to S140 shown in FIG. 1, or may execute steps S201 to S205 shown in FIG. 2, and so on.
  • the storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 521 and / or a cache storage unit 522, and may further include a read-only storage unit (ROM) 523.
  • RAM random access storage unit
  • ROM read-only storage unit
  • the storage unit 520 may further include a program / utility tool 524 having a set of (at least one) program modules 525.
  • program modules 525 include but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each of these examples or some combination may include an implementation of the network environment.
  • the bus 530 may represent one or more of several types of bus structures, including a storage unit bus or a storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local area using any of a variety of bus structures bus.
  • the electronic device 500 may also communicate with one or more external devices 700 (eg, keyboard, pointing device, Bluetooth device, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 500, and / or This enables the electronic device 500 to communicate with any device (eg, router, modem, etc.) that communicates with one or more other computing devices. Such communication may be performed through an input / output (I / O) interface 550. Moreover, the electronic device 500 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 560.
  • LAN local area network
  • WAN wide area network
  • public network such as the Internet
  • the network adapter 560 communicates with other modules of the electronic device 500 through the bus 530. It should be understood that although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device driver, redundant processing unit, external disk drive array, RAID system, tape drive And data backup storage system.
  • the example embodiments described herein can be implemented by software, or can be implemented by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, U disk, mobile hard disk, etc.) or on a network , Including several instructions to cause a computing device (which may be a personal computer, server, terminal device, or network device, etc.) to perform the method according to an exemplary embodiment of the present disclosure.
  • a computing device which may be a personal computer, server, terminal device, or network device, etc.
  • Exemplary embodiments of the present disclosure also provide a computer non-volatile readable storage medium on which is stored a program product capable of implementing the above method of this specification.
  • various aspects of the present disclosure may also be implemented in the form of a program product, which includes program code, and when the program product runs on the terminal device, the program code is used to cause the terminal device to execute The steps according to various exemplary embodiments of the present disclosure described in the "Exemplary Method" section.
  • a program product 600 for implementing the above method according to an exemplary embodiment of the present disclosure is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include a program code, and can be used in a terminal Devices, such as personal computers.
  • the program product of the present disclosure is not limited thereto, and in this document, the readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus, or device.
  • the program product may use any combination of one or more readable media.
  • the readable medium may be a readable signal medium or a readable storage medium.
  • the readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of readable storage media (non-exhaustive list) include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read only memory (ROM), erasable Programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the foregoing.
  • the computer-readable signal medium may include a data signal that is transmitted in baseband or as part of a carrier wave, in which readable program code is carried.
  • This propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above.
  • the readable signal medium may also be any readable medium other than a readable storage medium, and the readable medium may send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

Abstract

La présente invention appartient au domaine technique de l'intelligence artificielle, et concerne un procédé et un appareil de reconnaissance d'émotions dans un texte, un dispositif électronique, et un support d'enregistrement non volatil lisible par ordinateur. Le procédé consiste à : obtenir un ensemble de textes d'échantillons, l'ensemble de textes d'échantillons comprenant de multiples textes d'échantillons et des étiquettes de classification d'émotions correspondant aux multiples textes d'échantillons ; effectuer un calcul de correction sur un coût initial en fonction de la distribution du nombre des étiquettes de classification d'émotion dans l'ensemble de textes d'échantillons pour obtenir un coût de correction ; former un modèle d'apprentissage d'algorithme de levage au moyen de l'ensemble de textes d'échantillons et du coût de correction pour obtenir un modèle de reconnaissance d'émotions dans le texte ; et reconnaître un texte à reconnaître au moyen du modèle de reconnaissance d'émotions dans un texte pour obtenir le résultat de reconnaissance d'émotions dudit texte. Au moyen de la présente invention, la précision de reconnaissance et l'équilibre du texte de différentes catégories émotionnelles peuvent être améliorés, l'effet de reconnaissance est amélioré, et le procédé présente une forte applicabilité. (FIG. 3)
PCT/CN2019/089166 2018-10-24 2019-05-30 Procédé et appareil de reconnaissance d'émotions dans un texte, dispositif électronique, et support d'enregistrement non volatil lisible par ordinateur WO2020082734A1 (fr)

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