WO2019080863A1 - Procédé de classement de sentiments de texte, support d'informations et ordinateur - Google Patents

Procédé de classement de sentiments de texte, support d'informations et ordinateur

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Publication number
WO2019080863A1
WO2019080863A1 PCT/CN2018/111607 CN2018111607W WO2019080863A1 WO 2019080863 A1 WO2019080863 A1 WO 2019080863A1 CN 2018111607 W CN2018111607 W CN 2018111607W WO 2019080863 A1 WO2019080863 A1 WO 2019080863A1
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WIPO (PCT)
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underlying
feature vector
vector
classification
text
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PCT/CN2018/111607
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English (en)
Chinese (zh)
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曾伟波
郑耀松
倪时龙
苏江文
许成功
吕君玉
何天尝
林祥仙
Original Assignee
福建亿榕信息技术有限公司
国家电网有限公司
国网信息通信产业集团有限公司
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Publication of WO2019080863A1 publication Critical patent/WO2019080863A1/fr

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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
    • G06F40/237—Lexical tools

Definitions

  • the present invention relates to the field of machine learning, and in particular to a method and a storage medium for text sentiment classification.
  • a sentiment classification which is mainly used to analyze or predict the emotional category to which a text with emotional orientation belongs. Generally divided into positive, negative or positive, negative and neutral. According to the difference in size and granularity of the research object, the sentiment analysis technique can be roughly divided into the following three levels: word level, sentence level and chapter level emotion analysis.
  • the word-level sentiment classification can be divided into a dictionary-based sentiment classification model and a corpus-based sentiment classification model.
  • the dictionary-based sentiment classification model relies on the synonymous and antisense relations in the existing dictionary to judge the emotional tendency of words in the text. Some scholars use words such as "good” and “bad” as the benchmark words, and then calculate the difference between the mutual information between the registered words and the reference words. Some researchers use HowNet to detect the fuzzy emotion categories of adjectives in the text, and calculate the net coverage scores to distinguish the adjectives with uncertain emotion categories and the core adjectives determined by emotional categories.
  • the corpus-based sentiment classification model mainly identifies the sentiment orientation of words by statistical analysis of existing corpora. Some researchers have proposed a method based on the theory of emotional consistency.
  • Sentence-based sentiment classification can be divided into two sub-directions: semantic-based sentiment classification and statistical-based sentiment classification.
  • Semantic-based sentiment classification needs to match the sentiment dictionary to find the emotional words in the sentence, and then calculate the emotion of the whole sentence through the emotional intensity or polarity of the emotional words.
  • Some scholars try to use the rhetorical structure theory to solve the problem of sentiment orientation of sentences. Firstly, according to the theory, the sentences are divided into different blocks of text elements, and each element block is assigned different weights according to the importance of the overall emotion of the document. Emotional prediction is obtained by weighting the sentiment score of the sentence as a whole.
  • the statistical-based sentiment analysis method is based on the machine learning method.
  • a model is trained by the machine learning algorithm, and then the model is used to predict the emotional tendency of the unknown text data.
  • Some researchers try to construct feature vectors by using the number of positive and negative emotion words, negative words, special keywords, part-of-speech tags, and emojis, etc., and use machine learning to classify the sentiment data with emotional tendency.
  • the heat of learning some researchers use the recurrent neural network to combine the phrase vector and the word vector and send it into the classifier as a feature to analyze the sentiment orientation. The experiment proves the effectiveness of the method.
  • the inventors provide a text sentiment classification method, comprising the following steps: performing an emotional dictionary construction on an input text, the emotional dictionary construction step including a part-of-speech selection expression, an underlying feature vector extraction, a middle layer feature extraction, and a combination
  • the sentiment dictionary is collected, and the word vector of the training sample is collected, and the word vector of the training sample is pooled to obtain a middle layer feature vector; the underlying feature vector and the middle layer feature vector are weighted and merged to obtain a fusion feature vector, which is respectively based on the underlying feature vector
  • the classification model, the middle eigenvector classification model, and the fusion eigenvector classification model are used to calculate the classification results.
  • the underlying vector extraction is specifically performed by using a vector space model for the underlying features, wherein each dimension is characterized by a normalized TF-TDF weight.
  • the underlying feature vector and the middle layer feature vector are weighted and expressed as
  • the step of pooling the word vector comprises: dividing the number of dimensions of the underlying feature vector into several parts, summing the word vectors in each dimension, and then summing the summation results in order The order is combined to merge the results.
  • a text sentiment classification storage medium storing a computer program, when executed by a processor, implements the following steps: performing an emotional dictionary construction on the input text, the emotional dictionary construction step including a part-of-speech selection expression and an underlying feature vector extraction
  • the middle layer feature extraction combined with the sentiment dictionary, collects the word vector of the training sample, and pools the word vector of the training sample to obtain the middle layer feature vector; and performs weighted fusion on the bottom layer feature vector and the middle layer feature vector to obtain the fusion feature
  • the vector is calculated based on the underlying eigenvector classification model, the middle eigenvector classification model, and the fused feature vector classification model.
  • the underlying vector extraction is specifically performed by using a vector space model for the underlying features, wherein each dimension is characterized by a normalized TF-TDF weight.
  • the underlying feature vector and the middle layer feature vector are weighted and expressed as
  • the step of pooling the word vector further includes dividing the number of dimensions of the underlying feature vector into several parts, summing the word vectors in each dimension, and then summing the summation results in sequence The order is combined to merge the results.
  • a computer comprising the above described storage medium.
  • the present invention can establish an efficient and stable emotional dictionary with low dimension through learning, continue to use the emotional dictionary, and combine the feature fusion and the classifier fusion method to effectively improve the classification accuracy, through the bottom layer, The middle layer, the fusion feature vector, and the three classifiers to generate the classification result can make the final classification result more stable and more robust.
  • the calculation amount of the method of the present invention is also reduced by the detailed pooling process.
  • the present invention solves the problem that the prior art text emotion classification is not efficient and the classification accuracy is insufficient.
  • FIG. 1 is a flowchart of a text sentiment classification method according to an embodiment of the present invention.
  • FIG. 2 is a schematic diagram of a whole process of a text sentiment classification method according to an embodiment of the present invention
  • FIG. 3 is a diagram showing a pooling process according to an embodiment of the present invention.
  • FIG. 4 is a feature fusion diagram according to an embodiment of the present invention.
  • FIG. 1 is a text sentiment classification method.
  • the method is based on the sentiment classification model of the extreme learning machine.
  • the extreme learning machine is a single-hidden layer feedforward neural network (SLFNs).
  • the network consists of an input layer, a hidden layer and an output layer.
  • the input layer is hidden to the hidden layer and the hidden layer. There is a full connection between the output layers.
  • the method of the invention can begin in steps,
  • the sentiment dictionary construction step includes a part of speech selection expression and an underlying feature vector extraction.
  • the sentiment dictionary construction step includes two processes of part of speech selection and underlying feature selection.
  • Part of speech selection In the present invention, nouns, verbs, adjectives, and adverbs are collectively used as a reference word, and the sentiment dictionary can be a set of four word-of-speech reference words that appear in all the selected materials. Combine the words with different parts of speech to form the latent semantic information of a document, which can ensure the coverage of the sentiment dictionary to the greatest extent, while retaining the semantic information of the document.
  • Stratigraphic feature vector extraction uses the underlying feature selection principle based on chi-square statistics to further select the feature words that best represent the emotional polarity of the text.
  • the underlying feature selection vector space model is expressed, wherein the feature of each dimension in the vector is the normalized TF-IDF weight.
  • step S102 the layer feature extraction is combined with the sentiment dictionary to collect the word vector of the training sample, and the word vector of the training sample is pooled to obtain the middle layer feature vector; specifically, we can train the Skip-gram model in an unsupervised manner, and use The trained model inputs the training samples and generates a training sample word vector.
  • the specific pooling steps are shown in Figure 3:
  • each word vector group For each word vector group, the following operations are performed: all word vectors in the group are accumulated, and finally each word vector group forms a feature vector v(z), and the dimension of the feature vector is also k;
  • the present invention further performs step S104 to perform weighted fusion on the bottom layer feature vector and the middle layer feature vector to obtain a fusion feature vector
  • the S106 is respectively based on the underlying feature vector classification model, the middle layer feature vector classification model, and the fusion.
  • the eigenvector classification model calculates the classification result.
  • the specific process of classifying the sentiment to which the input sample belongs is: respectively feeding the underlying feature, the middle layer feature, and the fusion feature of the sample to be determined into the corresponding trained extreme learning machine.
  • the output result vectors of the three classification models are added together to obtain the final discriminant vector, and the median maximum corresponding label of the vector is the final emotion category.
  • the present invention can establish an efficient and stable emotional dictionary with low dimension through learning, continue to use the emotional dictionary, and combine the feature fusion and the classifier fusion method to effectively improve the classification accuracy, through the bottom layer, the middle layer, By merging the feature vectors and then generating the classification results through three classifiers, the final classification results can be made more stable and robust.
  • the calculation amount of the method of the present invention is also reduced by the detailed pooling process.
  • the present invention solves the problem that the prior art text emotion classification is not efficient and the classification accuracy is insufficient.
  • the underlying feature vector, the middle layer feature vector weighted fusion is expressed as,
  • steps may be performed before step S100 to preprocess the text to remove information that is irrelevant to the task, such as specification encoding format, removal of illegal characters, word segmentation, and part-of-speech tagging processing and stop word processing.
  • the canonical coding format is used for unified text encoding operations, such as unifying text content into UTF-8 encoding format; removing illegal characters can use regular expression matching to filter illegal characters; word segmentation tagging processing using ICTCLAS Chinese lexical analysis
  • the system performs word segmentation and part-of-speech tagging; stop word processing uses the stop word table to filter words that often appear in the text but have little meaning for sentiment analysis.
  • a text sentiment classification storage medium storing a computer program, when executed by a processor, implements the following steps: performing an emotional dictionary construction on the input text, the emotional dictionary construction step including a part-of-speech selection expression and an underlying feature vector extraction
  • the middle layer feature extraction combined with the sentiment dictionary, collects the word vector of the training sample, and pools the word vector of the training sample to obtain the middle layer feature vector; and performs weighted fusion on the bottom layer feature vector and the middle layer feature vector to obtain the fusion feature
  • the vector is calculated based on the underlying eigenvector classification model, the middle eigenvector classification model, and the fused feature vector classification model.
  • the underlying vector extraction is specifically performed by using a vector space model for the underlying features, wherein each dimension is characterized by a normalized TF-TDF weight.
  • the underlying feature vector and the middle layer feature vector are weighted and expressed as
  • the step of pooling the word vector further comprises: dividing the number of dimensions of the underlying feature vector into several parts, summing the word vectors in each dimension, and then summing the summation results in order Combine the summation results.
  • a computer comprising the above described storage medium.
  • the present invention solves the problem that the prior art text emotion classification is not efficient and the classification accuracy is insufficient.

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
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  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

La présente invention concerne un procédé de classement de sentiments de texte, un support d'informations et un ordinateur. Ledit procédé comprend les étapes suivantes consistant : à construire un dictionnaire de sentiments pour un texte d'entrée, l'étape de construction d'un dictionnaire de sentiments consistant à sélectionner et à exprimer des parties de parole, et à extraire des vecteurs de caractéristiques de niveau de base ; à extraire des caractéristiques de niveau moyen, et en association avec le dictionnaire de sentiments, à acquérir des vecteurs de mots d'échantillons d'apprentissage et à regrouper les vecteurs de mots des échantillons d'apprentissage, afin d'obtenir des vecteurs de caractéristiques de niveau moyen ; à réaliser une fusion pondérée sur les vecteurs de caractéristiques de niveau de base et les vecteurs de caractéristiques de niveau moyen, afin d'obtenir des vecteurs de caractéristiques fusionnés ; à calculer un résultat de classement sur la base d'un modèle de classement de vecteurs de caractéristiques de niveau de base, d'un modèle de classement de vecteurs de caractéristiques de niveau moyen et d'un modèle de classement de vecteurs de caractéristiques fusionnés. La présente invention résout le problème rencontré dans l'état de la technique selon lequel le classement de sentiments n'est pas suffisamment efficace et stable.
PCT/CN2018/111607 2017-10-26 2018-10-24 Procédé de classement de sentiments de texte, support d'informations et ordinateur WO2019080863A1 (fr)

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CN113011192A (zh) * 2021-03-16 2021-06-22 广东工业大学 基于注意力因果解释的文本情感特征提取方法
CN113312481A (zh) * 2021-05-27 2021-08-27 中国平安人寿保险股份有限公司 基于区块链的文本分类方法、装置、设备以及存储介质
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CN117521639A (zh) * 2024-01-05 2024-02-06 湖南工商大学 一种结合学术文本结构的文本检测方法
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