Enhance polarity classification on social media through sentiment-based feature expansion

Online social networking communities usually exhibit complex collective behaviors. Since emotions play a relevant role in human decision making, understanding how online networks drive human mood states become a task of considerable interest. One of the most relevant task in Sentiment Analysis is Polarity Classification, aimed at classifying the sentiment behind texts. We formulated different assumptions regarding which patterns within a message can be relevant sentiment indicators. Differently from well-formed texts, messages on social networks contain emoticons which could be strong sentiment indicators. For this, the first assumption states that the occurrences of emoticons representing a certain polarity could strongly agree with the overall message polarity. We then expanded the feature space including initialisms for emphatic and onomatopoeic expressions (e.g. bleh, wow, etc.) and "stretched words" (words with a letter repeated several times to emphasize a mood), extensively used in social media messages, because they could be useful information to help in determining the sentiment. Detailed analyses have been performed in order to support our assumptions. Four Machine Learning (supervised) classifiers are applied upon the expanded feature space model. Several experiments show that the considered features lead to increments of accuracy up to 5%.