Call for Paper - October 2019 Edition
IJCA solicits original research papers for the October 2019 Edition. Last date of manuscript submission is September 20, 2019. Read More

App Review Mining and Summarization

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Rabeya Sultana, Sujan Sarker
10.5120/ijca2018916918

Rabeya Sultana and Sujan Sarker. App Review Mining and Summarization. International Journal of Computer Applications 179(38):45-52, April 2018. BibTeX

@article{10.5120/ijca2018916918,
	author = {Rabeya Sultana and Sujan Sarker},
	title = {App Review Mining and Summarization},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2018},
	volume = {179},
	number = {38},
	month = {Apr},
	year = {2018},
	issn = {0975-8887},
	pages = {45-52},
	numpages = {8},
	url = {http://www.ijcaonline.org/archives/volume179/number38/29329-2018916918},
	doi = {10.5120/ijca2018916918},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

With the development of the web, online reviews are more important and essential information resource for people. Opinion mining and summarizing aims at extracting features and opinions and classify them as positive or negative. In this work, we develop a review mining and summarization technique and apply it to summarize the reviews of apps from Google Play App Store. Different from traditional text summarization, the features of apps are extracted based on customers opinions, classified them as positive or negative and ranked the apps based on the ranking of each feature. We propose two approaches, SentiWordNet 3.0 based and Naïve Bayes algorithm to classify opinions and find scores. The result of two approaches is quite similar. The experimental results show the effectiveness of the proposed approach in app review mining and summarizing.

References

  1. Opinion Mining: Exploiting the Sentiment of the Crowd,Diana Maynard, Adam Funk, Kalina Bontcheva. University of Sheffield, UK
  2. Sun, Shiliang, Chen Luo, and Junyu Chen. "A review of natural language processing techniques for opinion mining systems." Information Fusion 36 (2017): 10-25.
  3. Kumar, KM Anil, et al. "Effective Approaches for Classification and Rating of Users Reviews." Proceedings of International Conference on Cognition and Recognition. Springer, Singapore, 2018.
  4. https://play.google.com/store
  5. Kim, S.M., Hovy, E.: Determining the sentiment of opinions. In: Proceedings of the Coling Conference. (2004)
  6. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP). (2002)
  7. Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the ACL. (2004)
  8. Nasukawa, T., Yi, J.: Sentiment analysis: Capturing favorability using natural language processing. In: The Second International Conferences on Knowledge Capture. (2003)
  9. J. Yi, T. Nasukawa, R.B., Niblack, W.: Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In: 3rd IEEE Conf. on Data Mining (ICDM'03). (2003).
  10. L. Zhuang, F. Jing and X.-Y. Zhu, "Movie review mining and summarization," in Proceedings of the 15th ACM international conference on Information and knowledge management (CIKM '06), ACM, New York, NY, USA, 2006.
  11. H. Tang, S. Tan and X. Cheng, "A survey on sentiment detection of reviews," Expert Systems with Applications, vol. 36, no. 7, pp. 10760-10773, 2009.
  12. Bo Pang, Lillian Lee, "Opinion Mining and Sentiment Analysis", Foundations and Trends in Information Retrieval Vol. 2, Nos. 1–2 (2008).
  13. MikalaiTsytsarau, Themis Palpanas "Survey on mining subjective data on the web", Data Mining Knowledge Discovery, Springer 2012, pp.478-514.
  14. Pang B, Lee L, Vaithyanathan S. “Thumbs up? Sentiment classification using machine learning techniques”. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) 2002.
  15. Dave K, Lawrence S, Pennock D. “Mining the peanut gallery: opinion extraction and semantic classification of product reviews”. Proceedings of the 12th international conference on World Wide Web, ACM, New York, NY, USA, WWW’03.
  16. Bhatia, Surbhi, Manisha Sharma, and Komal Kumar Bhatia. "Sentiment Analysis and Mining of Opinions." Internet of Things and Big Data Analytics Toward Next-Generation Intelligence. Springer, Cham, 2018.503-523.
  17. M. Dragoni, C. da Costa Pereira, A.G.B. Tettamanzi, S. Villata, Smack: an argumentation framework for opinion mining, in: S. Kambhampati (Ed.), Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9–15 July 2016, IJCAI/AAAI Press, 2016, pp. 4242–4243
  18. Fellbaum, C. (1998). WordNet: An electronic lexical database. The MIT Press, 1998.
  19. AlexandraBalahur, MijailKabadjov, Josef Steinberger, Ralf Steinberger, Andrés Montoyo, "Challenges and solutions in the opinion summarization", Journal of Intelligent Information Systems Springer 2012.
  20. Siddhi Patni ,AvinashWadhe , “Review paper on sentiment analysis using web 2.0 by classification method “,IJARCS 2012.
  21. Chien-Liang Liu, Wen-Hoar Hsaio, Chia-Hoang Lee, Gen-Chi Lu, and Emery Jou” Movie Rating and Review Summarization in Mobile Environment”.
  22. Changbo Wang, Zhao Xiao, Yuhua Liu, YanruXu, Aoying Zhou, and Kang Zhang,”SentiView: Sentiment Analysis and Visualization for Internet Popular
  23. LisetteGarcía-Moya, Henry Anaya-Sánchez, and Rafael Berlanga-Llavori, “Retrieving Product Features and Opinions from Customer Reviews”, IEEE 2013.
  24. http://www.idc.com/prodserv/smartphone-os-market-share.jsp
  25. http://www.nltk.org/
  26. http://sentiwordnet.isti.cnr.it/
  27. Opinion Mining For Text Classification AnandMahendran, Anjali Duraiswamy, Amulya Reddy, Clayton Gonsalves School of Computing Science & Engineering, VIT University, Vellore, Tamilnadu, India.
  28. https://technowiki.wordpress.com/2011/08/28/bayesian-opinion-mining/
  29. https://play.google.com/store/apps/details?id=com.viber.voip
  30. http://sentiwordnet.isti.cnr.it/
  31. http://sentiwordnet.isti.cnr.it/search.php?q=awesome

Keywords

Opinion Mining, Sentiment Analysis, Summarization, App Review Analysis.