文章基本信息

标题：Emotion-Semantic-Enhanced Bidirectional LSTM with Multi-Head Attention Mechanism for Microblog Sentiment Analysis
本地全文：下载
作者：Shaoxiu Wang ; Yonghua Zhu ; Wenjing Gao 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2020
卷号：11
期号：5
页码：280-294
DOI：10.3390/info11050280
出版社：MDPI Publishing
摘要：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.
关键词：sentiment analysis; Chinese microblog; BiLSTM; multi-head attention; emoticons sentiment analysis ; Chinese microblog ; BiLSTM ; multi-head attention ; emoticons