Sentiment Classification Method of Chinese Internet Reviews Based on Ensemble Learning Framework

Abstract

The invention discloses a Chinese network review emotion classification method based on an integrated study frame. According to the method, a part-of-speech combination mode, an order-preserving sub-matrix mode and a frequent word sequence mode are adopted as input characteristics, in the level of characteristics, factors of the influence of Chinese word order information, interval phrase characteristics and the sentence length are considered, and the characteristic vector sparsity problem is solved through semantic similarities; the problem that many review text characteristics exist is solved, the inter-base-classifier independence is guaranteed, and the classification performance of base classifiers is improved as much as possible; a base classifier algorithm constructed based on product attributes is adopted to comprehensively review emotion information of each attribute in a text, and then the sentence-level emotional tendency of reviews is judged, so that a final classification result is more accurate. The Chinese network review emotion classification method based on the integrated study frame is applicable to e-commerce network review emotion classification in various fields, can make a potential consumer know evaluation information of a commodity before purchase and can also make a merchant better sufficiently know the consumer's opinion, and therefore the service quality is improved.

Description

technical field [0001] The invention belongs to the field of natural language processing technology and emotion computing, in particular to a Chinese network comment emotion classification method based on an integrated learning framework. Background technique [0002] With the development of the e-commerce industry, online shopping is more and more recognized by people, which also produces a large amount of online comment text data. Faced with these massive online reviews, on the one hand, consumers need to quickly understand the emotional tendencies of the reviews, get evaluation information about the item from other consumers’ experience, and optimize their purchase decisions; on the other hand, businesses also need to learn from consumers Summarize the market feedback information of the product from the emotional tendency of online comments, and improve the product. Therefore, how to sentimentally classify review texts has become an important research topic in the field ...

AI Technical Summary

Problems solved by technology

[0004] However, there are still three challenges in the sentiment classification task for Chinese online comment texts: First, Chinese online comments have the characteristics of diverse expressions and inconsistent sentence lengths. In feature extraction, TF-IDF as a representative The word weight calculation method is easy to cause problems such as lack of semantic information of text features and sparse feature vectors

Second, due to the complexity of the Chinese online comment text, the features extracted from it usually reach thousands. Although the classic RandomSubspace ensemble learning method can solve the problem of a large number of features, it is difficult to ensure that the basic Classifier performance

Third, a review text is likely to contain multiple product attribute words, and these attributes together determine the emotional tendency of the review. Previous sentence-level sentiment classification methods usually treat all attributes in the review as a whole, and do not analyze each attribute separately. With emotional information, it is easy to cause misjudgment of emotional categories

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