|
|
|
| Convolutional Neural Networks for Chinese Micro-blog Sentiment Analysis |
| LIU Longfei, YANG Liang, ZHANG Shaowu, LIN Hongfei |
Information Retrieval Laboratory, Dalian University of Technology, Dalian, Liaoning 116024, China)
|
|
|
|
Abstract Chinese micro-blog sentiment analysis aims to discover the user attitude towards hot events. This task is challenged by immense noises, rich new words, numerous abbreviations, vigorous collocation, together with the limited contextual information provided in the short texts. This paper explores the feasibility of performing Chinese micro-blog sentiment analysis by convolutional neural networks. To avoid task-specific features, character level embedding and word level embedding are adopted for convolutional neural networks(CNN). On the COAE 4th task corpus, the character level CNN achieves a sentiment prediction (in both binary positive/negative classification) accuracy of 95.42%, slightly better than the word level CNN yielding 94.65% accuracy. The results show that the convolutional neural networks model is promising in Chinese micro-blog sentiment analysis.
Key words deep learning;sentiment analysis;convolutional neural networks;word embedding
|
|
Received: 03 July 2015
|
|
|
|
|
|
|
|
|
2015, Vol. 29 
