Sentiment Analysis Based on Weighted Word2vec and Att-LSTM

Abstract

The Internet has become an indispensable part of modern people's lives. The sentiment analysis of text generated by the Internet has gradually become a research hot spot. Through the sentiment analysis of texts, information such as the public's emotional status, views on some social phenomena, and preferences for a product can be obtained. It contributes to commercial value and social stability. The common research methods are based on traditional machine learning algorithms. According to hand-labeled sentiment lexicons, we use machine learning algorithms such as naive Bayes, support vector machines, and maximum entropy methods are to perform sentiment analysis on textual information. To reduce the dependence on hand-built emotional dictionary and highlight the role of keywords in the review text, this paper proposes the weighted word2vec, adds the Attention mechanism to the Long-Short Term Memory (LSTM) model. Experiment result shows that the method is significantly better than traditional machine learning methods.