Research on Emotional Analysis of Tibetan Short Text Based on Fusion Sentiment Lexicon

Short-text sentiment analysis plays an integral role in predicting sentiment polarity. The current Tibetan short-text sentiment analysis model applies deep neural networks to learn some local grammatical structure information but ignores known sentiment lexicon information in modeling. Aiming at the above problems, this paper proposes a method for sentiment analysis of Tibetan short-text that incorporates sentiment lexicon information. The method firstly uses the word features extracted by Word2vec and Glove to fuse with the sentiment dictionary information, and inputs them into CNN & BiLSTM networks to extract text context features; secondly, adjusts the weights of the sentiment features through the self-attention mechanism; and finally, uses Softmax to infer the sentiment categories. The experimental results show that the accuracy of this method can reach 93.18% on the self-built sentiment analysis corpus. In addition, compared with the methods of extracting syllable features and word features, the accuracy of this paper's method can be improved by 4.92 and 3.43% respectively, which proves the effectiveness of the fused sentiment lexicon.