Expressing Sentiments in Game Reviews

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

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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Abstract

Opinion mining and sentiment analysis are important research areasof Natural Language Processing (NLP) tools and have become viable alternativesfor automatically extracting the affective information found in texts. Ouraim is to build an NLP model to analyze gamers’ sentiments and opinionsexpressed in a corpus of 9750 game reviews. A Principal Component Analysisusing sentiment analysis features explained 51.2 % of the variance of thereviews and provides an integrated view of the major sentiment and topic relateddimensions expressed in game reviews. A Discriminant Function Analysis basedon the emerging components classified game reviews into positive, neutral andnegative ratings with a 55 % accuracy.
Original languageEnglish
Title of host publicationArtificial Intelligence: Methodology, Systems, and Applications
Subtitle of host publication17th International Conference, AIMSA 2016, Varna, Bulgaria, September 7-10, 2016, Proceedings
EditorsChristo Dichev, Gennady Agre
PublisherSpringer
Pages352-355
ISBN (Print)978-3-319-44748-3
DOIs
Publication statusPublished - 27 Sep 2016
Externally publishedYes
EventInternational Conference on Artificial Intelligence: Methodology, Systems, and Applications: Artificial Intelligence: Methodology, Systems, and Applications - Varna, Bulgaria
Duration: 7 Sep 201610 Sep 2016
https://link.springer.com/book/10.1007/978-3-319-44748-3
https://www.springer.com/la/book/9783319447476

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSpringer
Volume9883

Conference

ConferenceInternational Conference on Artificial Intelligence: Methodology, Systems, and Applications
Abbreviated titleAIMSA 2016
CountryBulgaria
CityVarna
Period7/09/1610/09/16
Internet address

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Processing

Keywords

  • natural language processing
  • sentiment analysis
  • opinion mining
  • lexical analysis

Cite this

Secui, A., Sirbu, M-D., Dascalu, M., Crossly, S., Ruseti, S., & Trausan-Matu, S. (2016). Expressing Sentiments in Game Reviews. In C. Dichev, & G. Agre (Eds.), Artificial Intelligence: Methodology, Systems, and Applications: 17th International Conference, AIMSA 2016, Varna, Bulgaria, September 7-10, 2016, Proceedings (pp. 352-355). (Lecture Notes in Artificial Intelligence; Vol. 9883). Springer. https://doi.org/10.1007/978-3-319-44748-3_35
Secui, Anna ; Sirbu, Maria-Dorinela ; Dascalu, Mihai ; Crossly, Scott ; Ruseti, Stefan ; Trausan-Matu, Stefan. / Expressing Sentiments in Game Reviews. Artificial Intelligence: Methodology, Systems, and Applications: 17th International Conference, AIMSA 2016, Varna, Bulgaria, September 7-10, 2016, Proceedings. editor / Christo Dichev ; Gennady Agre. Springer, 2016. pp. 352-355 (Lecture Notes in Artificial Intelligence).
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Secui, A, Sirbu, M-D, Dascalu, M, Crossly, S, Ruseti, S & Trausan-Matu, S 2016, Expressing Sentiments in Game Reviews. in C Dichev & G Agre (eds), Artificial Intelligence: Methodology, Systems, and Applications: 17th International Conference, AIMSA 2016, Varna, Bulgaria, September 7-10, 2016, Proceedings. Lecture Notes in Artificial Intelligence, vol. 9883, Springer, pp. 352-355, International Conference on Artificial Intelligence: Methodology, Systems, and Applications, Varna, Bulgaria, 7/09/16. https://doi.org/10.1007/978-3-319-44748-3_35

Expressing Sentiments in Game Reviews. / Secui, Anna; Sirbu, Maria-Dorinela; Dascalu, Mihai; Crossly, Scott; Ruseti, Stefan; Trausan-Matu, Stefan.

Artificial Intelligence: Methodology, Systems, and Applications: 17th International Conference, AIMSA 2016, Varna, Bulgaria, September 7-10, 2016, Proceedings. ed. / Christo Dichev; Gennady Agre. Springer, 2016. p. 352-355 (Lecture Notes in Artificial Intelligence; Vol. 9883).

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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N2 - Opinion mining and sentiment analysis are important research areasof Natural Language Processing (NLP) tools and have become viable alternativesfor automatically extracting the affective information found in texts. Ouraim is to build an NLP model to analyze gamers’ sentiments and opinionsexpressed in a corpus of 9750 game reviews. A Principal Component Analysisusing sentiment analysis features explained 51.2 % of the variance of thereviews and provides an integrated view of the major sentiment and topic relateddimensions expressed in game reviews. A Discriminant Function Analysis basedon the emerging components classified game reviews into positive, neutral andnegative ratings with a 55 % accuracy.

AB - Opinion mining and sentiment analysis are important research areasof Natural Language Processing (NLP) tools and have become viable alternativesfor automatically extracting the affective information found in texts. Ouraim is to build an NLP model to analyze gamers’ sentiments and opinionsexpressed in a corpus of 9750 game reviews. A Principal Component Analysisusing sentiment analysis features explained 51.2 % of the variance of thereviews and provides an integrated view of the major sentiment and topic relateddimensions expressed in game reviews. A Discriminant Function Analysis basedon the emerging components classified game reviews into positive, neutral andnegative ratings with a 55 % accuracy.

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KW - opinion mining

KW - lexical analysis

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Secui A, Sirbu M-D, Dascalu M, Crossly S, Ruseti S, Trausan-Matu S. Expressing Sentiments in Game Reviews. In Dichev C, Agre G, editors, Artificial Intelligence: Methodology, Systems, and Applications: 17th International Conference, AIMSA 2016, Varna, Bulgaria, September 7-10, 2016, Proceedings. Springer. 2016. p. 352-355. (Lecture Notes in Artificial Intelligence). https://doi.org/10.1007/978-3-319-44748-3_35