Paper 5: Thumbs up?: sentiment classification using machine learning techniques

Title: Thumbs up?: sentiment classification using machine learning techniques
Authors: Bo Pang, Lillian Lee, Shivakumar Vaithyanathan
Link: http://dl.acm.org/citation.cfm?id=1118693.1118704

Due to the recent emergence of online opinion voting sites such as Rotten Tomatoes, the researchers in this paper attempted to develop a system of using machine learning to express user sentiment. Using their system, the researchers are able to analyze a text-based review for a movie, and determine how the reviewer felt without the author assigning a fixed score. Using these analysis, their reviewing system can determine if the aggregate feedback across multiple reviewers is positive, negative, or neutral.

Their results were incredibly promising, the machine learning algorithm beating all prior algorithms, as well as a simple random search. The one area their algorithm struggled was correctly identifying sentiments in reviews with a "narrative" structure to them. When authors start a review with statements such as, "I went into this movie expecting to hate it", but then end with an overall positive review, the system has issues detecting the true sentiment from their author's earlier stated expectations.