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Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/7478
Title: Extracting Gamers' Opinions from Reviews
Authors: Sirbu, Dorinela
Secui, Ana
Dascalu, Mihai
Crossley, Scott
Ruseti, Stefan
Trausan-Matu, Stefan
Keywords: Natural Language Processing
sentiment analysis
opinion mining
game reviews
lexical analysis
Issue Date: Feb-2017
Publisher: IEEE
Citation: Sirbu, M. D., Secui, A., Dascalu, M., Crossley, S. A., Ruseti, S., & Trausan-Matu, S. (2016). Extracting Gamers' Opinions from Reviews. In 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2016) (pp. 227–232). Timisoara, Romania: IEEE
Abstract: Opinion mining and sentiment analysis are a trending research domain in Natural Language Processing focused on automatically extracting subjective information, feelings, opinions, ideas or emotions from texts. Our study is centered on identifying sentiments and opinions, as well as other latent linguistic dimensions expressed in on-line game reviews. Over 9500 entertainment game reviews from Amazon were examined using a Principal Component Analysis applied to word-count indices derived from linguistic resources. Eight affective components were identified as being the most representative semantic and sentiment-oriented dimensions for our dataset. These components explained 51.2% of the variance of all reviews. A Multivariate Analysis of Variance showed that five of the eight components demonstrated significant differences between positive, negative and neutral game reviews. These five components used as predictors in a Discriminant Function Analysis, were able to classify game reviews into positive, negative and neutral ratings with a 55% accuracy.
URI: http://hdl.handle.net/1820/7478
Appears in Collections:1. RAGE Publications

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