Micro-blogging Sentiment Detection by Collaborative Online Learning

Publication Type

Conference Proceeding Article

Publication Date

12-2010

Abstract

We study the online micro-blog sentiment detection problem, which aims to determine whether a micro-blog post expresses emotions. This problem is challenging because a micro-blog post is very short and individuals have distinct ways of expressing emotions. A single classification model trained on the entire corpus may fail to capture characteristics unique to each user. On the other hand, a personalized model for each user may be inaccurate due to the scarcity of training data, especially at the very beginning where users have just posted a few entries. To overcome these challenges, we propose learning a global model over all micro-bloggers, which is then leveraged to continuously refine the individual models through a collaborative online learning way. We evaluate our algorithm on a real-life micro-blog dataset collected from the popular micro-blog site – Twitter. Results show that our algorithm is effective and efficient for timely sentiment detection in real micro-blogging applications

Discipline

Computer Sciences | Databases and Information Systems | Social Media

Publication

IEEE 10th International Conference on Data Mining ICDM 2010: 13-17 December, 2010, Sydney, Australia: Proceedings

First Page

893

Last Page

898

ISBN

9780769542560

Identifier

10.1109/ICDM.2010.139

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

http://dx.doi.org/10.1109/ICDM.2010.139

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