Supposing we wanted to broadly classify the sentiment of a text as positive or negative, we may choose to model the opinion mining task as a classification problem, such as could be solved with supervised machine learning techniques like a Naïve Bayes classifier (NBC). Given a set of positive text features and negative text features, an NBC strategy will allow us to take a new text and classify it as being more positive or more negative given the observations about other similar texts we have made in the past. The machine learning literature is replete with examples of supervised classification, and it is a very reliable approach for certain types of problems.
The trick of course with this type of classification scheme is being able to count on the observations we have made in the past as reliable indicators of future observations. These training examples are critically important and are the basis for the success of the entire scheme. After all, if we choose...
