Skip to main content
SpringerLink
Log in

Comprehensive Exploration of Game Reviews Extraction and Opinion Mining Using NLP Techniques

  • Conference paper
  • First Online:
Fourth International Congress on Information and Communication Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1041))

Abstract

Sentiment analysis and opinion summarization have become an important research area with the increase of available data on the Web. Since the Internet started containing more and more opinions and reviews for different products, individual users and companies saw the benefits of a priori evaluations based on other users’ experiences; thus, automated analyses centered on customer impressions and experiences emerged as crucial marketing instruments. Our aim is to create a scalable and easily extensible pipeline for building a custom-tailored sentiment analysis model for a specific domain. A corpus of around 200,000 games reviews was extracted, and three state-of-the-art models (i.e., support vector machines, multinomial Naïve-Bayes, and deep neural network) were employed in order to classify the reviews into positive, neutral, and negative. Current results surpass previous experiments based on word counts applied on a similar game reviews dataset, thus arguing for the adequacy of the proposed workflow.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.tensorflow.org/hub/modules/google/universal-sentence-encoder/2.

  2. 2.

    http://scikit-learn.org.

References

  1. B. Liu, Sentiment Analysis and Opinion Mining (Morgan & Claypool Publishers, San Rafael, CA, 2012)

    Book  Google Scholar 

  2. C.J. Hutto, E. Gilbert, Vader: a parsimonious rule-based model for sentiment analysis of social media text, in 8th International AAAI Conference on Weblogs and Social Media (AAAI Press, Ann Arbor, MI, 2014), pp. 216–225

    Google Scholar 

  3. Z. Hailong, G. Wenyan, J. Bo, Machine learning and lexicon based methods for sentiment classification: a survey, in 2014 11th Web Information System and Application Conference (WISA) (IEEE, 2014) pp. 262–265

    Google Scholar 

  4. B. Pang, L. Lee, Opinion mining and sentiment analysis (foundations and trends (R) in Information Retrieval). Now Publishers Inc. (2008)

    Google Scholar 

  5. B. Liu, Sentiment analysis and opinion mining. Synth Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)

    Article  Google Scholar 

  6. B. Pang, L. Lee, Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)

    Article  Google Scholar 

  7. O.K.M. Cheng, R.Y.K. Lau, Probabilistic language modelling for context-sensitive opinion mining. Sci. J. Inf. Eng. 5(5), 150–154 (2015)

    Google Scholar 

  8. J.G. Shanahan, Y. Qu, J. Wiebe, Computing Attitude and Affect in Text: Theory and applications, vol. 20 (Springer,Berlin, 2006)

    Google Scholar 

  9. A. Hogenboom, F. Boon, F. Frasincar, A statistical approach to star rating classification of sentiment, Management Intelligent Systems (Springer, 2012), pp. 251–260

    Google Scholar 

  10. M.M. Bradley, P.J. Lang, Affective Norms for English words (ANEW): Stimuli, Instruction Manual and Affective Ratings, (The Center for Research in Psychophysiology, University of Florida, Gainesville, FL, 1999)

    Google Scholar 

  11. P. Stone, D.C. Dunphy, M.S. Smith, D.M. Ogilvie, Associates: The General Inquirer: A Computer Approach to Content Analysis (The MIT Press, Cambridge, MA, 1966)

    Google Scholar 

  12. H.D. Lasswell, J.Z. Namenwirth, The Lasswell Value Dictionary (Yale University Press, New Haven, 1969)

    Google Scholar 

  13. K.R. Scherer, What are emotions? And how can they be measured? Soc. Sci. Inf. 44(4), 695–729 (2005)

    Article  Google Scholar 

  14. S.M. Mohammad, P.D. Turney, Crowdsourcing a word–emotion association lexicon. Comput. Intell 29(3), 436–465 (2013)

    Article  MathSciNet  Google Scholar 

  15. S. Crossley, K. Kyle, D.S McNamara, Sentiment Analysis and Social Cognition Engine (SEANCE): An Automatic Tool for Sentiment, Social Cognition, and Social Order Analysis. Behavior Research Methods (in press)

    Google Scholar 

  16. M.-D. Sirbu, A. Secui, M. Dascalu, S.A. Crossley, S. Ruseti, S. Trausan-Matu, Extracting gamers’ opinions from reviews, in 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2016) (IEEE, Timisoara, Romania, 2016), pp. 227–232

    Google Scholar 

  17. A. Pak, P. Paroubek, Twitter as a corpus for sentiment analysis and opinion mining, in LREC 2010 (Valletta, Malta, 2010)

    Google Scholar 

  18. A. Go, R. Bhayani, L. Huang, Twitter Sentiment Classification Using Distant Supervision. CS224N Project Report, vol. 1(2) (Stanford, 2009)

    Google Scholar 

  19. P. Melville, W. Gryc, R.D. Lawrence, Sentiment analysis of blogs by combining lexical knowledge with text classification, in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, 2009), pp. 1275–1284

    Google Scholar 

  20. T. Mullen, N. Collier, Sentiment analysis using support vector machines with diverse information sources, in Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (2004)

    Google Scholar 

  21. C.D. Manning, M. Surdeanu, J. Bauer, J. Finkel, S.J. Bethard, D. McClosky, The Stanford CoreNLP Natural Language Processing Toolkit, in Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL, Baltimore, MA , 2014), pp. 55–60

    Google Scholar 

  22. R. Socher, A. Perelygin, J.Y. Wu, J. Chuang, C.D. Manning, A.Y. Ng, C.P. Potts, Recursive deep models for semantic compositionality over a sentiment treebank, in Conference on Empirical Methods in Natural Language Processing (EMNLP 2013) (ACL, Seattle, WA, 2013)

    Google Scholar 

  23. H.D. Kim, K. Ganesan, P. Sondhi, C. Zhai, Comprehensive Review of Opinion Summarization (2011)

    Google Scholar 

  24. B. Liu, L. Zhang, A survey of opinion mining and sentiment analysis, in Mining Text Data (Springer, 2012), pp. 415–463

    Google Scholar 

  25. L. Zhuang, F. Jing, X.-Y. Zhu Movie review mining and summarization, in Proceedings of the 15th ACM International Conference on Information and Knowledge Management (ACM, 2006), pp. 43–50

    Google Scholar 

  26. Y. Ganjisaffar, Crawler4j–Open Source Web Crawler for Java, Google Scholar (2012)

    Google Scholar 

  27. C. Gormley, Z. Tong, Elasticsearch: The Definitive Guide: A Distributed Real-Time Search and Analytics Engine (O’Reilly Media, Inc. California, 2015)

    Google Scholar 

  28. Y. Gupta, Kibana Essentials, Packt Publishing Ltd (2015)

    Google Scholar 

  29. D. Cer, Y. Yang, S.-y. Kong, N. Hua, N. Limtiaco, R.S. John, N. Constant, M. Guajardo-Cespedes, S. Yuan, C. Tar, Universal Sentence Encoder. arXiv preprint (2018), arXiv:1803.11175

  30. A. Secui, M.-D. Sirbu, M. Dascalu, S.A. Crossley, S. Ruseti, S. Trausan-Matu, Expressing sentiments in game reviews, in 17th International Conference on Artificial Intelligence: Methodology, Systems, and Applications (AIMSA 2016) (Springer, Varna, Bulgaria, 2016), pp. 352–355

    Google Scholar 

Download references

Acknowledgements

This work was supported by a grant of the Romanian Ministry of Research and Innovation, CCCDI—UEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0689/“Lib2Life—Revitalizarea bibliotecilor si a patrimoniului cultural prin tehnologii avansate”/“Revitalizing Libraries and Cultural Heritage through Advanced Technologies”, within PNCDI III.

Author information

Authors and Affiliations

  1. University Politehnica of Bucharest, Splaiul Independenței 313, 60042, Bucharest, Romania

    Stefan Ruseti, Maria-Dorinela Sirbu, Mihnea Andrei Calin, Mihai Dascalu, Stefan Trausan-Matu & Gheorghe Militaru

  2. Academy of Romanian Scientists, Splaiul Independenţei 54, 050094, Bucharest, Romania

    Mihai Dascalu & Stefan Trausan-Matu

Authors
  1. Stefan Ruseti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maria-Dorinela Sirbu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mihnea Andrei Calin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mihai Dascalu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stefan Trausan-Matu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gheorghe Militaru
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mihai Dascalu .

Editor information

Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, UK

    Xin-She Yang

  2. University of Reading, Reading, UK

    Simon Sherratt

  3. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, Gujarat, India

    Amit Joshi

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ruseti, S., Sirbu, MD., Calin, M.A., Dascalu, M., Trausan-Matu, S., Militaru, G. (2020). Comprehensive Exploration of Game Reviews Extraction and Opinion Mining Using NLP Techniques. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Fourth International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 1041. Springer, Singapore. https://doi.org/10.1007/978-981-15-0637-6_27

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-0637-6_27

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0636-9

  • Online ISBN: 978-981-15-0637-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation