Emotion data classifying method and system

Abstract

The invention provides an emotion data classifying method and system. The method comprises the steps that a document-document graph and a word-word graph corresponding to a training data set are established, in the document-document graph, nodes signify documents in the training data set, the geometrical information of edges signifies the relevance between the documents, in the word-word graph, nodes signify words in the training data set, and the geometrical information of edges signifies the relevance between the words; regularization items based on the graphs are established according to the geometrical information of the document-document graph and the word-word graph; an objective function is subjected to optimizing processing, and a document-emotion matrix is output; the documents in a testing data set are acquired, and the emotional tendency corresponding to the documents in the testing data set is acquired according to the document-emotion matrix. The emotion data classifying method and system are used, and the emotion classifying precision can be improved.

Description

technical field [0001] The invention relates to natural language processing technology, in particular to an emotion data classification method and system. Background technique [0002] With the development of Web 2.0, more and more users generate emotional data in web pages, which usually exist in the form of comments and blog data in the network. Sentiment classification refers to automatically predicting the sentiment orientation of sentiment data generated by users, for example, predicting whether a comment is positive or negative. [0003] Recently, sentiment classification has gained widespread attention in natural language processing, and sentiment classification methods can be divided into supervised sentiment analysis and unsupervised sentiment analysis. Supervised sentiment analysis relies on human-annotated training data, and in some cases, the labeling work is time-consuming and expensive, which motivates unsupervised or semi-supervised sentiment analysis. [00...

AI Technical Summary

Problems solved by technology

However, it is difficult to define a universally optimal emotional vocabulary to cover all words from different domains

Furthermore, most semi-automatic dictionary-based methods do not yield satisfactory results

The traditional more advanced dictionary-based method is based on the constrained non-negative matrix tri-factorization (Constrained Non-negative Matrix Tri-factorization, referred to as CNMTF) sentiment classification method, which uses domain-independent sentiment vocabulary as prior knowledge. Sentiment classification, however, experiments show that the sentiment classification accuracy of the CNMTF-based sentiment classification method still needs to be improved

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