Micro-blogging Sentiment Detection by Collaborative Online Learning
Conference Proceeding Article
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
Computer Sciences | Databases and Information Systems | Social Media
Data Management and Analytics
IEEE 10th International Conference on Data Mining ICDM 2010: 13-17 December, 2010, Sydney, Australia: Proceedings
City or Country
LI, Guangxia; HOI, Steven C. H.; CHANG, Kuiyu; and JAIN, Ramesh.
Micro-blogging Sentiment Detection by Collaborative Online Learning. (2010). IEEE 10th International Conference on Data Mining ICDM 2010: 13-17 December, 2010, Sydney, Australia: Proceedings. 893-898. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2361