Abstract:

At present, in the research of Chinese text emotion analysis, the method based on semantic rules and emotion dictionary usually needs to set the emotional threshold manually.However, the method based on deep learning can’t fully extract emotional features because it fails to use language knowledge such as semantic rules and emotional dictionary.As to shortcomings of two methods, a text emotion analysis method combining language knowledge and deep learning was proposed.Firstly, the key emotional segments in the text were extracted according to the semantic rules.Secondly, more explicit emotion words were extracted from the key emotional segments according to the emotional dictionary to construct the emotion set.Thirdly, the deep level features were extracted from the original text, key emotional segments and emotional set by using the deep learning model.Finally, the features were weighted and fused, and the classifier was used to judge the emotional polarity.The experimental results show that compared with the deep learning model without language knowledge, this method has significantly improved the ability of emotional polarity classification.

Key words: text sentiment analysis, language knowledge, deep learning