期刊
IEEE ACCESS
卷 7, 期 -, 页码 38856-38866出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2905048
关键词
Long short-term memory (LSTM); stacked bi-directional LSTM; sentiment analysis; continuous bag-of-words; Chinese microblog; contextual features
资金
- National Natural Science Foundation of China (NFSC) [61672170, 61871313]
- NSFC-Guangdong Joint Fund [U1401251]
- Science and Technology Planning Project of Guangdong Province [2015B090923004, 2017A050501035]
- Science and Technology Program of Guangzhou [201807010058]
- Guangdong Science and Technology Plan [2015B090923004]
Sentiment analysis on Chinese microblogs has received extensive attention recently. Most previous studies focus on identifying sentiment orientation by encoding as many word properties as possible while they fail to consider contextual features (e.g., the long-range dependencies of words), which are, however, essentially important in the sentiment analysis. In this paper, we propose a Chinese sentiment analysis method by incorporating a word2vec model and a stacked bidirectional long short-term memory (Stacked Bi-LSTM) model. We first employ the word2vec model to capture semantic features of words and transfer words into high-dimensional word vectors. We evaluate the performance of two typical word2vec models: continuous bag-of-words (CBOW) and skip-gram. We then use the Stacked Bi-LSTM model to conduct the feature extraction of sequential word vectors. We next apply a binary softmax classifier to predict the sentiment orientation by using semantic and contextual features. Moreover, we also conduct extensive experiments on the real dataset collected from Weibo (i.e., one of the most popular Chinese microblogs). The experimental results show that our proposed approach achieves better performance than other machine-learning models.
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