MORE SENSE: MOvie REviews SENtiment analysis boosted with Semantics

Sentiment analysis is becoming one of the most active area in Natural Language Processing nowadays. Its importance coincides with the growth of social media and the open space they create for expressing opinions and emotions via reviews, forum discussions, microblogs, Twitter and social networks. Most of the existing approaches on sentiment analysis rely mainly on the presence of affect words that explicitly reflect sentiment. However, these approaches are semantically weak, that is, they do not take into account the semantics of words when detecting their sentiment in text. Only recently a few approaches (e.g. sentic computing) started investigating towards this direction. Following this trend, this paper investigates the role of semantics in sentiment analysis of movie reviews. To this end, frame semantics and lexical resources such as BabelNet are employed to extract semantic features from movie reviews that lead to more accurate sentiment analysis models. Experiments are conducted with different types of semantic information by assessing their impact in movie reviews dataset. A 10-fold cross-validation shows that F1 measure increases slightly when using semantics in sentiment analysis in social media. Results show that the proposed approach considering word's semantics for sentiment analysis is a promising direction.