Word-level sentiment analysis with reinforcement learning
Ruiqi Chen, Yanquan Zhou, Liujie Zhang, Xiuyu Duan · IOP Conference Series Materials Science and Engineering · 2019
Reinforcement learning (RL) imitates how human perceive the word and acquire knowledge. We manage to propose a RL method to realize text sentiment analysis. We describe a new framework named Word-level Sentiment LSTM (WS-LSTM), which means we use such framework to get sentiment tendency for each word in a sentence. We suppose Positive, Neural and Negative as actions and establish three different LSTM tunnels for each action. When we choose an action for a word, relative LSTM tunnel will be chosen to handle the input. After traversing a whole sentence, we get both word-level sentiment sequence and a sentence-level representation. Such representation is used in classification and we success in getting a sentence-level sentiment analysis. Results show that our method can get sentiment for each word in a specific task. As to the word-level analysis, results from several datasets show that our method plays an acceptable job.