Micro-blogging Sentiment Detection by Collaborative Online Learning

• 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: Computer Sciences; Databases and Information Systems; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/2361; http://dx.doi.org/10.1109/ICDM.2010.139
• Institution: Singapore Management University

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