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Movies Recommendation Based on Opinion Mining in Twitter
ARMENTANO, MARCELO G.; SCHIAFFINO, SILVIA N.; CHRISTENSEN, INGRID; BOATO, FRANCISCO
Conferencia; 14th Mexican International Conference on Artificial Intelligence; 2015
A traditional way for movie recommendation in a real scenario is by word of mouth. People ask their friends or relatives their opinion about a movie and then make their own judgment about whether to go to see the movie. In this article, we take this paradighm to evaluate Twitter as a source of information for movie recommendation. We built a balanced dataset consisting of 3036 tweets expresing opinions regarding movies. Then, we evaluated dierent tokenization strategies, pre-processing techniques and algorithms to build classication models that are able to determine the sentiment (opinion + polarity) expressed in the short texts published in Twitter. Finally, the best classier is used to extract the main sentiment of Twitter users regarding a target movie in order to help users to decide to see the movie or not, obtaining romisingresults.