Approaches to subjectivity and sentiment analysis often rely on manually or automatically constructed lexicons. Most such lexicons are compiled as lists of words, rather than word meanings ("senses"). However, many words have both subjective and objective senses as well as senses of different polarities, which is a major source of ambiguity in subjectivity and sentiment analysis. The proposed work addresses this gap, by investigating novel methods for subjectivity sense labeling, and exploiting the results in sense-aware subjectivity and sentiment analysis. To achieve these goals, three research objectives are targeted. The first is developing methods for assigning subjectivity labels to word senses in a taxonomy. The second is developing contextual subjectivity disambiguation techniques to effectively make use of the word sense subjectivity annotations. The third is applying these techniques to multiple languages, including languages with fewer resources than English. The project will have broader impacts in both research and education. First, it will make subjectivity and sentiment resources and tools more widely available, in multiple languages, to the research community, which will help advance the state of the art in automatic subjectivity analysis, which in turn will benefit end applications. Second, several educational goals will be pursued: training graduate and undergraduate students in computational linguistics; augmenting artificial intelligence courses with projects based on the proposed research, which will offer students hands-on experience with natural language processing research; and reaching out to women and minorities to increase their exposure to text processing technologies and access to research opportunities.
