I am facing a sentiment analysis task where I am using Naive Bayes to classify documents as Positive, Negative or Neutral. I have thought of using Information Gain as my filter for feature selection. Taking into account that I need to classify into 3 classes, would it be ok to use IG without further modifications (since I think IGain works best with 2 classes)?
Information gain is a reasonable objective to use for selecting features (even when there are multiple classes). Note that information gain is a traditional metric for selecting decision attributes for building decision trees. Note that a classic problem with decision tress is when to stop adding decision nodes---too many nodes usually leads to poor generalization. IG will help you determine an ordering of features from most useful to least useful. You will need another method (such as evaluation on a hold-out set) to determine a cut-off point.
You may be interested in reading A Comparative Study on Feature Selection in Text Categorization (1997), which evaluates IG against other methods.
Note that your problem sounds more like ordinal regression (which encodes an ordering in the labels) than regular classification.