TASC:Topic-Adaptive Sentiment Classification on Dynamic Tweets

Abstract

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
(TASC) model, which starts with a classifier, built on common features and mixed labeled data from various topics. It
minimizes the hinge loss to adapt to unlabeled data and features including topic-related sentiment words, authors’
sentiments and sentiment connections derived from “@” mentions of tweets, named as topic-adaptive features. Text and
non-text features are extracted and naturally split into two views for co-training. The TASC learning algorithm updates
topic-adaptive features based on the collaborative selection of unlabeled data, which in turn helps to select more reliable
tweets to boost the performance. We also design the adapting model along a timeline (TASC-t) for dynamic tweets. An
experiment on 6 topics from published tweet corpuses demonstrates that TASC outperforms other well-known supervised
and ensemble classifiers. It also beats those semi-supervised learning methods without feature adaption. Meanwhile,
TASC-t can also achieve impressive accuracy and F-score. Finally, with timeline visualization of “river” graph, people
can intuitively grasp the ups and downs of sentiments’ evolvement, and the intensity by color gradation.