Date of Award
Master of Science (MS)
This thesis uses a naive bayes sentiment classifier to analyze six semesters of homework review data from CSE427S. Experiments describe the benefits of an automated classification system and explore original ways of reducing the number of features and reviews. A new algorithm is proposed that tries to take advantage of aspects of the review data that limit classification accuracy. This analysis can be used to help guide the process of automatically using short reviews to understand student sentiment.
Marion Neumann Yevgeniy Vorobeychik Ron Cytron