A New Method for Sentiment Classification on Weibo
Xianyun Tian, Guang Yu, Yongtian Yu, Pengyu Li, Jiayin Pei
International Journal of Computational Linguistics Research, Vol. 7, No. 1, 2016.
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Abstract:
Sentiment classification of posts on Weibo is a key to analyse peopleâ€™s opinions and attitudes toward products,
services, and social events. It is also at the core of many other natural language processing tasks. In this paper, we
apply a new feature representation technique to building a sentiment classifier and classifying the posts. Firstly, we
crawled a set of posts from Weibo and labelled them. Then, we cleaned the data to remove the noisy information and
transformed the varying-length posts into fixed-sized input vectors based on five different feature engineering techniques.
Finally, we evaluated the performances of the five different feature representation techniques on a same data set
with the use of support vector machines, naive Bayes and classification and regression tree. Experimental results demonstrate
that our new method is efficient and outperforms the other ones.