In this problem, we will develop a sentiment analysis tool. We have provided a data set containing short customer reviews, or snippets of reviews, for products. Each has been labeled as a positive or negative review. For instance, below is an example of a positive review
<p>i downloaded a trial version of computer associates ez firewall and antivirus and fell in love with a computer security system all over again</p>
and a negative one
<p>i dont especially like how music files are unstructured ; basically they are just dumped into one folder with no organization , like you might have in windows explorer folders and subfolders</p>
from the training set. We will use Support Vector Machines to learn to classify such review sentences into positive and negative classes.

We will use the word occurrence features: if a particular word (or more generally, a token, which may include punctuation, numbers, etc.) w occurs in an example, the corresponding feature is set to 1, otherwise to 0.