Sentiment Analysis of Weibo Comment Texts Based on Extended Vocabulary and Convolutional Neural Network

In the era of big data and Internet, social network platforms, blogs, and recommender systems generate thousands of subjective information every day. The emotional content of these information may be related to books, characters, commodities, activities and so on. Analyzing and mining subjective emotional information is conducive to personal decision making, enterprise reform, and government’s public opinion regulation. In this paper, based on Weibo comment texts, we use the network term and the wiki Chinese data set to expand the original vocabulary, train word embeddings and realize the sentence-level sentiment classification based on the convolution neural network. At the same time, an optimization method according to the statement length of pooling layer is put forward. The method is proved to be effective with high accuracy on our data set.