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.
Bibliographical noteFunding Information:
This work was supported in part by the National Natural Science Foundation of China (NFSC) under Grant 61672170 and Grant 61871313, in part by the NSFC-Guangdong Joint Fund under Grant U1401251, in part by the Science and Technology Planning Project of Guangdong Province under Grant 2015B090923004 and Grant 2017A050501035, in part by the Science and Technology Program of Guangzhou under Grant 201807010058, and in part by the Guangdong Science and Technology Plan under Grant 2015B090923004.
© 2019 IEEE.
- Chinese microblog
- contextual features
- continuous bag-of-words
- Long short-term memory (LSTM)
- sentiment analysis
- stacked bi-directional LSTM