权融合字词向量的中文在线评论情感分析.

The widespread use of social networking platforms has led to the emergence of emotionally rich online comment texts, analyzing the emotions expressed in comments is of great significance to companies, platforms, etc. In order to solve the current problem of weak feature extraction ability and ignoring the emotional information of short text in online comment short text sentiment analysis, this paper proposed a model based on text sentiment value weighted char-word mixture word representation-SVW-BERT. First, it based on the fusion of character and word level vectors represented text vectors for maximizing semantic representation. At the same time, considering the influence of adverbs, negative words, exclamation sentences and interrogative sentences on the sentiment of the text, it used the weight to calculate the sentiment value of the text, and constructed sentiment analysis model of Chinese short text based on text sentiment value weighted char-word mixture word representation. Through the network platform online reviews data set, it validated the feasibility and the advantages of the model. The experimental results show that the char-word mixture word representation is stronger in semantic extraction, and the sentiment value weighted sentence vector considers the sentiment information contained in the text itself, which achieves the effect of improving the ability of sentiment classification. [ABSTRACT FROM AUTHOR]