Master of Science, The Ohio State University, 2012, Computer Science and Engineering

Sentiment analysis is a task of mining subjective information expressed in text, and has received a lot of focus from the research community in Natural Language Processing in recent years. With the rapid growth of social networks, sentiment analysis is becoming much more attractive to Natural Language Processing researchers. Identifying words or phrases that carry sentiments is a crucial task in sentiment analysis. The work in this thesis concentrates on automatically constructing polarity lexicons for sentiment analysis on social networks. One of the challenges in sentiment analysis on social networks is the lack of domain-dependent polarity lexicons and there is a need for automatically constructing sentiment lexicons for any specific domain. Two proposed methods are based on graph propagation and topic modeling. Our experiments confirm the quality of the polarity lexicons constructed using these two algorithms.

Committee: Rajiv Ramnath Professor (Advisor); Jay Ramanathan Professor (Committee Member) Subjects: Computer Science