View article

ZHANG, ZHE. Text Mining for Sentiment Analysis.(Under the direction of Professor Munindar P. Singh.)

Over the past few years, with the development of web services and the emergence of userdriven social media, more and more people express their sentiments publicly, generating a large amount of opinionated data. Sentiment analysis is such a research field that aims at developing automated approaches to accurately extract sentiments from opinionated data. Researchers have devoted considerable attention to this field. However, due to the complexity and diversity of linguistic expressions, we are still far from a satisfying solution. In this dissertation, we have identified four challenges that may hinder current research progress: basic sentiment expressing unit, paucity of labeled data, domain dependence, and author modeling. Accordingly, we propose two approaches, ReNew and Arch, to address these challenges.