Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 42, Issue 4, Pages 1857-1863Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2014.09.011
Keywords
Sentiment classification; Word2vec; SVMperf; Semantic features
Categories
Funding
- National Natural Science Foundation of China [61175110]
- National Basic Research Program of China [2012CB316305]
Ask authors/readers for more resources
Since the booming development of e-commerce in the last decade, the researchers have begun to pay more attention to extract the valuable information from consumers comments. Sentiment classification, which focuses on classify the comments into positive class and negative class according to the polarity of sentiment, is one of the studies. Machine learning-based method for sentiment classification becomes mainstream due to its outstanding performance. Most of the existing researches are centered on the extraction of lexical features and syntactic features, while the semantic relationships between words are ignored. In this paper, in order to get the semantic features, we propoie a method for sentiment classification based on word2vec and SVMperf. Our research consists of two parts of work. First of all, we use word2vec to cluster the similar features for purpose of showing the capability of word2vec to capture the semantic features in selected domain and Chinese language. And then, we train and classify the comment texts using word2vec again and SVMperf. In the process, the lexicon-based and part-of-speech-based feature selection methods are respectively adopted to generate the training file. We conduct the experiments on the data set of Chinese comments on clothing products. The experimental results show the superior performance of our method in sentiment classification. (C) 2014 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available