Abstract— TASC:Topic-Adaptive Sentiment Classification on Dynamic Tweets. Sentiment classification is a topic-sensitive task, i.e., a classifier trained from one topic will perform worse on another. This is especially a problem for the tweets sentiment analysis.Since the topics in Twitter are very diverse, it is impossible to train a universal classifier for all topics. Moreover,compared to product review, Twitter lacks data labeling and a rating mechanism to acquire sentiment labels. The extremely sparse text of tweets also brings down the performance of a sentiment classifier. In this paper, we propose a semi-supervised topic-adaptive sentiment classification < Final Year Projects 2016 > model, which starts with a classifier built on common features and mixed labeled data from various topics.
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