Sentiment Analysis based on Bi-LSTM using Tone

In view of most of sentiment analysis texts are too short to get enough textual features, a method of bidirectional Long Short-Term Memory using tone (Word, Character and Tone model based on Bidirectional Long Short-Term Memory, WCT-Bi-LSTM) was proposed. Distinguished from the general method of sentiment analysis only taking word as the feature, the model also used character and tone features as input to enrich the characteristics of the text. After that, the model integrated the deep semantic meaning of word, character and tone. It could better grasp the emotion of the text and improve the accuracy of sentiment classification. The experimental results show that, compared with the model which does not integrate tone, the accuracy of the proposed model is increased by 1.2% and 0.9% on two experimental datasets respectively, which proves that the proposed method can effectively improve the accuracy of sentiment classification.