标题：Sentiment Analysis of Comment Texts Based on BiLSTM
作者：Xu, Guixian; Meng, Yueting; Qiu, Xiaoyu; Yu, Ziheng; Wu, Xu
作者机构：[Xu, Guixian; Meng, Yueting; Yu, Ziheng; Wu, Xu] Minzu Univ China, Coll Informat Engn, Beijing 100081, Peoples R China.; [Qiu, Xiaoyu] Shandong Univ 更多
通讯作者：Xu, GX;Xu, Guixian
通讯作者地址：[Xu, GX]Minzu Univ China, Coll Informat Engn, Beijing 100081, Peoples R China.
关键词：Sentiment analysis; artificial intelligence; social network; weighted; word vectors; BiLSTM
摘要：With the rapid development of Internet technology and social networks, a large number of comment texts are generated on the Web. In the era of big data, mining the emotional tendency of comments through artificial intelligence technology is helpful for the timely understanding of network public opinion. The technology of sentiment analysis is a part of artificial intelligence, and its research is very meaningful for obtaining the sentiment trend of the comments. The essence of sentiment analysis is the text classification task, and different words have different contributions to classification. In the current sentiment analysis studies, distributed word representation is mostly used. However, distributed word representation only considers the semantic information of word, but ignore the sentiment information of the word. In this paper, an improved word representation method is proposed, which integrates the contribution of sentiment information into the traditional TF-IDF algorithm and generates weighted word vectors. The weighted word vectors are input into bidirectional long short term memory (BiLSTM) to capture the context information effectively, and the comment vectors are better represented. The sentiment tendency of the comment is obtained by feedforward neural network classifier. Under the same conditions, the proposed sentiment analysis method is compared with the sentiment analysis methods of RNN, CNN, LSTM, and NB. The experimental results show that the proposed sentiment analysis method has higher precision, recall, and F-1 score. The method is proved to be effective with high accuracy on comments.