Movie reviews: do words add up to a sentiment?

Sentiment analysis, the automatic extraction of opinion from text, has been enjoying some attention in the media during the national elections. In this thesis, we will discuss the classification of movie reviews as ’thumbs up’ or ’thumbs down’. Movie reviews are interesting and difficult because of the wide range of topics in movies. The reviews are HTML web pages, which poses an interesting challenge for preprocessing and noise removal. We describe the reviews as ’bags of words’ and use support vector machines (SVMs) for classification, as well as transductive support vector machines, which require less training data. To model topics in the reviews, a latent semantic analysis (LSA) was done on a large set of movie reviews. The results show that it is hard to improve SVM performance with latent semantic analysis. The discussion of the results provide some insights into why no performance increase was achieved.