Tweet Sentiment Analysis by Incorporating Sentiment Specific Word Embedding and Weighted Text Features
Previous studies have used many manually identified features and word embeddings for tweet sentiment classification. In this paper, we propose a new approach, which incorporates sentiment specific word embeddings (SSWE) and a weighted text feature model (WTFM) WTFM produces features based on text negation, tf.idf weighting scheme, and a Rocchio text classification method. Compared to other tweet sentiment feature generation approaches, WTFM is easy to build, simple, yet effective. Experiments show that the proposed approach outperforms the two state of the art tweet sentiment classification methods, SSWE and National Research Council Canada's (NRC) model.
2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)