Attention Enhanced Bi-directional LSTM for Sentiment Analysis
GUAN Pengfei1, LI Bao‘an1, LV Xueqiang1, ZHOU Jianshe2
1.Beijing Key Laboratory of Internet Culture & Digital Dissemination Research, Beijing Information Science & Technology University, Beijing 100101, China; 2.Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China
Abstract：To deal with sentiment analysis at the sentence level, this paper proposes a method of attention enhanced Bi-directional LSTM. It employs attention mechanism to learn every word weight distribution of sentiment tendency directly from the word vector. Tested on the NLPCC 2014 sentiment analysis dataset, the results of the model outperfroms the other sentence level sentiment classification model.
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2019, Vol. 33 
