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Emotion-Semantic-Enhanced Bidirectional LSTM with Multi-Head Attention Mechanism for Microblog Sentiment Analysis

Shanghai Film Academy, Shanghai University, Shanghai 200072, China
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Information 2020, 11(5), 280; https://doi.org/10.3390/info11050280
Received: 26 April 2020 / Revised: 20 May 2020 / Accepted: 20 May 2020 / Published: 22 May 2020
(This article belongs to the Section Artificial Intelligence)
The sentiment analysis of microblog text has always been a challenging research field due to the limited and complex contextual information. However, most of the existing sentiment analysis methods for microblogs focus on classifying the polarity of emotional keywords while ignoring the transition or progressive impact of words in different positions in the Chinese syntactic structure on global sentiment, as well as the utilization of emojis. To this end, we propose the emotion-semantic-enhanced bidirectional long short-term memory (BiLSTM) network with the multi-head attention mechanism model (EBILSTM-MH) for sentiment analysis. This model uses BiLSTM to learn feature representation of input texts, given the word embedding. Subsequently, the attention mechanism is used to assign the attentive weights of each words to the sentiment analysis based on the impact of emojis. The attentive weights can be combined with the output of the hidden layer to obtain the feature representation of posts. Finally, the sentiment polarity of microblog can be obtained through the dense connection layer. The experimental results show the feasibility of our proposed model on microblog sentiment analysis when compared with other baseline models. View Full-Text
Keywords: sentiment analysis; Chinese microblog; BiLSTM; multi-head attention; emoticons sentiment analysis; Chinese microblog; BiLSTM; multi-head attention; emoticons
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Wang, S.; Zhu, Y.; Gao, W.; Cao, M.; Li, M. Emotion-Semantic-Enhanced Bidirectional LSTM with Multi-Head Attention Mechanism for Microblog Sentiment Analysis. Information 2020, 11, 280.

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