A Comparison of Techniques for Sentiment Classification of Film Reviews
This work addresses sentiment analysis for film reviews, but it is incremental as it compares existing methods without introducing new paradigms.
The paper compared lexicon-based and machine learning approaches for sentiment classification of film reviews, finding that machine learning techniques were superior, though a simple lexicon-based method achieved good results and more features did not necessarily improve performance.
We undertake the task of comparing lexicon-based sentiment classification of film reviews with machine learning approaches. We look at existing methodologies and attempt to emulate and improve on them using a 'given' lexicon and a bag-of-words approach. We also utilise syntactical information such as part-of-speech and dependency relations. We will show that a simple lexicon-based classification achieves good results however machine learning techniques prove to be the superior tool. We also show that more features do not necessarily deliver better performance as well as elaborate on three further enhancements not tested in this article.