Studies in sentiment analysis and opinion mining have examined how different features are effective in polarity classification by making use of positive, negative or neutral values. However, the identification of extreme opinions (most negative and most positive opinions) have overlooked in spite of their wide significance in many applications.
In our study, we will combine empirical features (e.g. bag of words, word embeddings, polarity lexicons, and set of textual features) so as to identify extreme opinions and provide a comprehensive analysis of the relative importance of each set of features using hotel reviews.
Keywords: Sentiment Analysis, Opinion Mining, linguistic features, Classification, Extreme Opinion