Abstract
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 paradigm to evaluate Twitter as a source of information for movie recommendation. We built a balanced dataset consisting of 3036 tweets expressing opinions regarding movies. Then, we evaluated different tokenization strategies, pre-processing techniques and algorithms to build classification models that are able to determine the sentiment (opinion + polarity) expressed in the short texts published in Twitter. Finally, the best classifier 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 promising results.
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This study was partially supported by research projects PICT-2011-0366 and PICT-2014-2750
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Armentano, M.G., Schiaffino, S., Christensen, I., Boato, F. (2015). Movies Recommendation Based on Opinion Mining in Twitter. In: Pichardo Lagunas, O., Herrera Alcántara, O., Arroyo Figueroa, G. (eds) Advances in Artificial Intelligence and Its Applications. MICAI 2015. Lecture Notes in Computer Science(), vol 9414. Springer, Cham. https://doi.org/10.1007/978-3-319-27101-9_6
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DOI: https://doi.org/10.1007/978-3-319-27101-9_6
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