Chinese sentiment analysis method based on BERT, LSTM and CNN fusion

Abstract

The invention provides a Chinese sentiment analysis method based on BERT, LSTM and CNN fusion. The Chinese sentiment analysis method comprises the steps: performing text preprocessing on a plurality of Chinese corpora in a Chinese corpus data set to obtain a plurality of sequences corresponding to the plurality of Chinese corpora; extracting word embedding of each sequence by using a BERT model; performing feature extraction on each sequence by adopting BERT, LSTM and CNN to obtain text deep semantic features corresponding to each sequence; and classifying the obtained text deep semantic features by using a softmax classifier to train and test the model so as to realize sentiment polarity prediction analysis. According to the technology, the defects in the prior art can be overcome, and the accuracy of Chinese text sentiment analysis is improved.

Description

technical field [0001] The invention relates to information processing technology, in particular to a Chinese emotion analysis method based on the fusion of BERT, LSTM and CNN. Background technique [0002] In recent years, with the rapid development of network technology, many consumers have begun to express their opinions and comments on a certain thing on the Internet, and natural language processing technology has emerged as the times require. Tasks such as sentiment analysis, such as positive and negative analysis of commodity reviews, Sensitive content analysis, user-interested content analysis, and even abnormal access log analysis in the security field can actually be done in the form of text classification, which is essentially a text output with multiple corresponding tags. Therefore, how to quickly and accurately analyze user opinions and emotions from massive information has become an important research topic in the field of information technology, and has very i...

AI Technical Summary

Problems solved by technology

[0005] At present, the granularity of sentiment analysis is relatively large, which generally refers to the analysis and judgment of the entire sentence or text, so it is easy to ignore the finer-grained information in the text and sentence, lose a lot of valuable information, and cannot accurately judge the meaning of the text

The traditional marking of text is generally manual, which consumes a lot of time and human resources. This method affects the subsequent training effect and greatly reduces the accuracy of sentiment classification.

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