Microblog Rumor Detection Based on Comment Sentiment and CNN-LSTM

摘要

Traditional rumor detection methods, such as feature engineering, are difficult and time-consuming. Moreover, the user page structure of Sina Weibo includes not only the content text, but also a large amount of comment information, among which the sentimental characteristics of comment are difficult to learn by neural network. In order to solve these problems, a rumor detection method based on comment sentiment and CNN-LSTM is proposed, and long short-term memory (LSTM) is connected to the pooling layer and full connection layer of convolutional neural network (CNN). Meanwhile, comment sentiment is added to rumor detection model as an important feature. The effectiveness of this method is verified by experiments.