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....

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2015-06, Vol.27 (6), p.1696-1709
Main Authors: Liu, Shenghua, Cheng, Xueqi, Li, Fuxin, Li, Fangtao
Format: Article
Language:English
Subjects:
Adaptation models
adaptive feature
Algorithms
Classification
Classifiers
cross-domain
Data mining
Data visualization
Dynamics
Feature based
Feature extraction
Google
Learning
multiclass SVM
Product reviews
Sentiment analysis
sentiment classification
Support vector machines
Texts
topic-adaptive
Twitter
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Summary: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.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2014.2382600