Abstract. Weibo emotion recognition is one of the main tasks of the study of social public opinion. BI-LSTM, as a derivative model of RNN, has been widely used in the task of text emotion analysis. However, existing models do not make good use of prior information sun as emotion words and emoji, and we always capture the different keywords in order to gain a different understanding of the text. Therefore, this paper proposes a Multi-view and Attention-Based BI-LSTM method for weibo emotion recognition. Firstly, we use the emotion ontology lexicon and weibo emotions to label each word in a sentence. Secondly, we use these labels as attention information, combine the attention mechanism and BI-LSTM to get the sentiment words perspective and emoticon perspective. Finally, the output of the original semantic perspective of BI-LSTM is fused with the output of the above two perspectives to enhance the performance of the classification algorithm. Experiments show that the proposed method has a 6% increase in macro-average F1 score and an increase of 8% in micro-average F1 score compared with a AVE-BI-LSTM output in the task of weibo emotion recognition.
In multiview, Cai and Hao REF proposed a multiview and attention-based BI-LSTM method for Weibo emotion recognition.
Multi-view and Attention-Based BI-LSTM for Weibo Emotion Recognition
Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
Computer Science