Microblog-oriented Multi-scale CNN Multi-label Sentiment Classification Model
Xinyu Cao, Liangwen Hou, Haitao Wang, Lei Liu · 2020
With the rapid development of the Internet, social networks have become an important platform for people to express their opinions. Through the sentiment analysis of the text, the user's emotional tendency can be captured in time. Multi-label sentiment classification is one of the difficult issues. Therefore, the paper proposes a multi-label sentiment classification model for short texts of Weibo based on multiscale CNN. Firstly, aiming at the insufficiency of multi-label emotional corpus, we give a method to construct a multi-label emotional corpus based on emotional seed words, which uses synonym forest and TF-IDF weights to automatically label the corpus. Then a multi-label sentiment analysis model based on multi-scale CNN is proposed, which uses different convolution scales to extract features of text. Finally we found that the accuracy of the method has been significantly improved.