Learning Sentiment Lexicons with applications to Recommender Systems

Search is now going beyond looking for factual information and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information particularly in online commerce. The main problem that the present book addresses is how to model text to find meaningful words that express a sentiment. In this context I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. In this approach we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. Sentiment lexicons can be applied in a broad set of applications however popular recommendation algorithms have somehow been disconnected from sentiment analysis. We present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm and a study that observes the viability of using a domain-specific lexicon to compute entities reputation.