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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Main Authors: | , , , |
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Format: | text |
Language: | English |
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Institutional Knowledge at Singapore Management University
2010
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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 |
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 |
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