Voting based classification method for cross-language subjective and objective sentiments

Abstract

The present invention discloses a voting based classification method for cross-language subjective and objectives sentiments. The method comprises the following steps of: S1, constructing a target language sentiment dictionary according to a source language sentiment dictionary; S2, separately adopting three algorithms of a rule algorithm, a combination algorithm of machine translation and statistical machine learning, and a polarity eigenvalue calculation algorithm to extract words from a sentence of a to-be-tagged text, determining sentiment polarity of the words according to the constructed target language sentiment dictionary, and further determining subjective and objective nature of the sentence; and S3, acquiring subjective and objective nature determination results of the sentence, obtained according to the three algorithms, and determining the subjective and objective nature of the sentence by voting. The method can fully consider contexts and usage habits of a target language on the premise of keeping certain accuracy, effectively solves the problem that a tagged corpus of the target language is scarce, and further improves classification accuracy on the premise of ensuring the recall rate.

Description

technical field [0001] The invention relates to a cross-language subjective and objective emotion classification method, in particular to a voting-based cross-language subjective and objective emotion classification method, which belongs to the technical field of computer natural language processing. Background technique [0002] With the rapid development of social network platforms such as Weibo, text sentiment classification technology has become a hot spot in text information processing. Annotated emotional resources provide a basis for text emotion recognition research. At present, the corpus resources in the English field include SentiWordNet, fine-grained sentiment analysis corpus MPQA, etc.; in the Chinese field, there are HowNet (HowNet), Synonyms Cilin, etc. However, the distribution of annotated corpora across languages ​​is uneven. When there is a lack of annotated corpus in a certain language, it has become a hot topic to use annotated corpus in other language...

AI Technical Summary

Problems solved by technology

The sentiment dictionary obtained by this method is inaccurate and cannot meet the accuracy requirements of cross-language sentiment analysis

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