Extracting Gamers' Opinions from Reviews

Maria-Dorinela Sirbu, Anna Secui, Mihai Dascalu, Scott Crossley, Stefan Ruseti, Stefan Trausan-Matu

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademic

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.
Original languageEnglish
Title of host publicationSYNASC 2016
Subtitle of host publication18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
EditorsJames Davenport, Viorel Negru, Tetsuo Ida, Tudor Jebelean, Stephen Watt, Daniela Zahaire
PublisherIEEE
Pages 227-232
ISBN (Electronic)978-1-5090-5707-8
DOIs
Publication statusPublished - Feb 2017
Externally publishedYes
EventSYNASC 2016: 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing - Timisoara, Romania
Duration: 24 Sep 201627 Sep 2016
https://synasc.ro/2016

Publication series

NameSYNASC
PublisherIEEE Computer Society
ISSN (Print)2470-881X

Conference

ConferenceSYNASC 2016
Abbreviated titleSYNASC2016
CountryRomania
CityTimisoara
Period24/09/1627/09/16
Internet address

Fingerprint

Linguistics
Analysis of variance (ANOVA)
Principal component analysis
Semantics
Processing
Multivariate Analysis

Keywords

  • Natural Language Processing
  • sentiment analysis
  • opinion mining
  • game reviews
  • lexical analysis

Cite this

Sirbu, M-D., Secui, A., Dascalu, M., Crossley, S., Ruseti, S., & Trausan-Matu, S. (2017). Extracting Gamers' Opinions from Reviews. In J. Davenport, V. Negru, T. Ida, T. Jebelean, S. Watt, & D. Zahaire (Eds.), SYNASC 2016: 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (pp. 227-232). (SYNASC). IEEE. https://doi.org/http://10.0.4.85/SYNASC.2016.044, https://doi.org/10.1109/SYNASC.2016.38
Sirbu, Maria-Dorinela ; Secui, Anna ; Dascalu, Mihai ; Crossley, Scott ; Ruseti, Stefan ; Trausan-Matu, Stefan. / Extracting Gamers' Opinions from Reviews. SYNASC 2016: 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. editor / James Davenport ; Viorel Negru ; Tetsuo Ida ; Tudor Jebelean ; Stephen Watt ; Daniela Zahaire. IEEE, 2017. pp. 227-232 (SYNASC).
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title = "Extracting Gamers' Opinions from Reviews",
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.",
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Sirbu, M-D, Secui, A, Dascalu, M, Crossley, S, Ruseti, S & Trausan-Matu, S 2017, Extracting Gamers' Opinions from Reviews. in J Davenport, V Negru, T Ida, T Jebelean, S Watt & D Zahaire (eds), SYNASC 2016: 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. SYNASC, IEEE, pp. 227-232, SYNASC 2016, Timisoara, Romania, 24/09/16. https://doi.org/http://10.0.4.85/SYNASC.2016.044, https://doi.org/10.1109/SYNASC.2016.38

Extracting Gamers' Opinions from Reviews. / Sirbu, Maria-Dorinela; Secui, Anna; Dascalu, Mihai; Crossley, Scott; Ruseti, Stefan; Trausan-Matu, Stefan.

SYNASC 2016: 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. ed. / James Davenport; Viorel Negru; Tetsuo Ida; Tudor Jebelean; Stephen Watt; Daniela Zahaire. IEEE, 2017. p. 227-232 (SYNASC).

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademic

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AB - 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.

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Sirbu M-D, Secui A, Dascalu M, Crossley S, Ruseti S, Trausan-Matu S. Extracting Gamers' Opinions from Reviews. In Davenport J, Negru V, Ida T, Jebelean T, Watt S, Zahaire D, editors, SYNASC 2016: 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. IEEE. 2017. p. 227-232. (SYNASC). https://doi.org/http://10.0.4.85/SYNASC.2016.044, https://doi.org/10.1109/SYNASC.2016.38