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Title: Expressing Sentiments in Game Reviews
Authors: Secui, Ana
Sirbu, Maria-Dorinela
Dascalu, Mihai
Crossley, Scott
Ruseti, Stefan
Trausan-Matu, Stefan
Keywords: natural language processing
sentiment analysis
opinion mining
lexical analysis
Issue Date: 2016
Publisher: Springer
Citation: Secui, A., Sirbu, M. D., Dascalu, M., Crossley, S. A., Ruseti, S., & Trausan-Matu, S. (2016). Expressing Sentiments in Game Reviews. In 17th Int. Conf. on Artificial Intelligence: Methodology, Systems, and Applications (AIMSA 2016) (pp. 352–355). Varna, Bulgaria: Springer
Abstract: Opinion mining and sentiment analysis are important research areas of Natural Language Processing (NLP) tools and have become viable alternatives for automatically extracting the affective information found in texts. Our aim is to build an NLP model to analyze gamers’ sentiments and opinions expressed in a corpus of 9750 game reviews. A Principal Component Analysis using sentiment analysis features explained 51.2 % of the variance of the reviews and provides an integrated view of the major sentiment and topic related dimensions expressed in game reviews. A Discriminant Function Analysis based on the emerging components classified game reviews into positive, neutral and negative ratings with a 55 % accuracy.
Appears in Collections:1. RAGE Publications

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