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Emotion classification method and emotion classification system

A sentiment classification and sentiment dictionary technology, applied in the fields of user sentiment analysis and cross-cultural communication, it can solve the problems of difficult text, obscure sentiment expression, manual labeling of training sets, etc., and achieve the effect of improving the accuracy.

Active Publication Date: 2016-08-31
BEIJING FOREIGN STUDIES UNIVERSITY
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Problems solved by technology

The reason is that the topics involved in the field of cross-cultural communication include politics, economy, school, credit, and the world. After data capture research, it is found that Internet users’ emotional expressions on these topics are relatively subtle, and their emotional tendencies are not as good as those in the field of product or movie reviews. Strong, so it is difficult to manually label this article to obtain a training set by using a two-class or three-class method

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  • Emotion classification method and emotion classification system

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Embodiment Construction

[0053] The technical solutions of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

[0054] The emotion classification of the present invention mainly has two methods based on rules and based on statistics. Due to the continuous appearance of new words, changes in expression methods and complex language processing, rule-based sentiment classification methods are difficult to apply. Statistical-based sentiment classification methods use machine learning methods and text representation models. Among them, the machine learning methods mainly used in sentiment analysis include: Naive Bayesian (Naive Bayesian), K-Nearest Neighbor (KNN), Support Vector Machine (Support Vector Machine, SVM). The text representation model mainly adopts the vector space model (vector space model, VSM). VSM believes that documents are represented in the dictionary space, that is, a document is a one-to-many mapping, expressed a...

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Abstract

The invention relates to an emotion classification method and an emotion classification system. The classification method comprises the following steps: preprocessing the data of a sample to be tested; getting a feature word set of the sample to be tested; using a naive Bayes algorithm to perform calculation on the feature word set of the sample to be tested to generate the probability that the feature word set of the sample to be tested belongs to a category; and using a support vector machine to correct the probability that the feature word set of the sample to be tested belongs to a category, and determining the classification of the sample to be tested. According to the invention, Internet user emotion under the perspective of cross-cultural communication is analyzed in a more fine-grained way by building an emotional dictionary and emotion feature words in the field of cross-cultural communication, and the accuracy of emotion classification is improved.

Description

technical field [0001] The invention relates to the fields of cross-cultural communication and user emotion analysis, in particular to an emotion classification method and system. Background technique [0002] Intercultural refers to the communication activities between individuals, groups or organizations from different cultural backgrounds. The study of cross-cultural communication in my country began in the 1980s. The early research mainly focused on the relationship between foreign language teaching and culture, and then gradually expanded to the communication between people with different cultural backgrounds and the methods of promoting cross-cultural communication. In the early days, there was a lack of data collection tools for cross-cultural communication, which greatly limited the empirical research on cross-cultural communication. Today, the Internet, as an emerging media carrier and communication channel, plays an increasingly important role in the initiation an...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06F17/27
CPCG06F16/35G06F16/374G06F40/289
Inventor 徐月梅王子厚冯驿曾颖菲刘苗苗
Owner BEIJING FOREIGN STUDIES UNIVERSITY
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