Text sentiment analysis based on long short-term memory - Dan Li; Jiang Qian (2016 First IEEE International Conference on Computer Communication and the Internet (ICCCI), 2016)
With the rapid development of Internet and big explosion of text data, it has been a very significant research subject to extract valuable information from text ocean. To realize multi-classification for text sentiment, this paper promotes a RNN language model based on Long Short Term Memory (LSTM), which can get complete sequence information effectively. Compared with the traditional RNN language model, LSTM is better in analyzing emotion of long sentences. And as a language model, LSTM is applied to achieve multi-classification for text emotional attributes. So though training different emotion models, we can know which emotion the sentence belongs to by using these emotion models. And numerical experiments show that it can produce better accuracy rate and recall rate than the conventional RNN.