Optimizing N-gram based text feature selection in sentiment analysis for commercial products in Twitter through polarity lexicons

This study aims to optimize N-gram based text feature selection in sentiment analysis for commercial products in twitter through polarity lexicons. This can be done by merging dictionary-based weighing with naïve-Bayes classification of sentiments. The study is still ongoing but partial results show potential.