  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.
