Micro-blogging Sentiment Detection by Collaborative Online Learning

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 th...

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Bibliographic Details
Main Authors: LI, Guangxia, HOI, Steven C. H., CHANG, Kuiyu, JAIN, Ramesh
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2010
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/2361
http://dx.doi.org/10.1109/ICDM.2010.139
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Institution: Singapore Management University
Language: English
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Summary: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