First, let's examine the precision of the classifiers. Remember that the precision is how well the classifiers do at only returning positive reviews. This indicates the percentage of reviews that each classifier has identified as being positive is actually positive in the test set:
We need to remember a couple of things while looking at this graph. First, sentiment analysis is difficult, compared to other categorization tasks. Most importantly, human raters only agree about 80 percent of the time. So, the bar seen in the preceding figure that almost reaches 65 percent is actually decent, if not great. Still, we can see that the naive Bayesian classifier generally outperforms the maxent one for this dataset, especially when using unigram features. It performed less well for the bigram and trigram features, and slightly lesser for the POS-tagged unigrams.
We didn't try tagging the bigram and trigrams with POS information, but that might have been an interesting experiment...
