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 goal of the proposed work is to address this gap, by investigating novel methods for subjectivity sense labeling, and exploiting the results in sense-aware subjectivity and sentiment analysis. To achieve this goal, we target the following four research objectives. First, we will develop new methods for assigning subjectivity labels to word senses in a taxonomy. Second, we will develop contextual subjectivity disambiguation techniques that will effectively make use of the word sense subjectivity annotations. Third, we will explore the application of these techniques to multiple languages, including languages with fewer resources than English. Finally, we plan to demonstrate the usefulness of our research results through an end application for crosscultural tracking of opinions and sentiments.

This project is funded by the National Science Foundation.