Abstract
The rapidly increasing volume of reviews for the different mobile applications (apps) makes it almost impossible to analyze user feedback reviews manually. Reviews can contain star ratings, emotions, and text reviews for a proposed feature, a bug report, and/or a confidentiality protest. Stakeholders can benefit from reviews after analyzing them using Sentiment Analysis (SA) as user requirements, ideas for improvements, user sentiments about specific features, and descriptions of experiences with these features. This paper investigates the field of research of using Arabic SA for mobile apps’ requirements evolution. We assembled answers from the literature for four Research Questions (RQs) we formulated. The results revealed three main points. First, the use of SA trends in general for mobile apps’ requirements evolution research can be grouped to automating extraction of future requirements, applying ranking frameworks to classify reviews to informative and non-informative, and proposing visualization techniques for users’ feedback. Second, there turned to be many current challenges that face the field of using Arabic SA for user comments of mobile apps’ requirements evolution because of the inherent challenges of three intersecting fields. Finally, there is little proof that there is any study conducted till now that applies the use of Arabic SA on user comments of mobile apps for the purpose of requirements evolution.
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References
Gao, C., Xu, H., Hu, J., Zhou, Y.: AR-tracker: track the dynamics of mobile apps via user review mining. In: Proceedings of International Workshop on Internet based Virtual Computing Environment (IVCE), San Francisco, USA, pp. 284–290 (2015)
Guzman, E., Maalej, W.: How do users like this feature? A fine grained sentiment analysis of app reviews. In: Proceedings of the 22nd International Conference on Requirements Engineering (RE) 2014, Karlskrona, pp. 153–162 (2014)
Tran, L.M.S., Massacci, F., Mylopoulos, J.: Requirement evolution: towards a methodology and framework. In: Proceedings of CaiSE 2011 Doctoral Consortium (2011)
Tan, S.C.: Supporting Visualization and Analysis of Requirements Evolution. Master, University of Edinburgh (2009)
Felici, M.: Observational Models of Requirements Evolution. Ph.D. University of Edinburgh (2004)
El-Beltagy, S.R., Ali, A.: Open issues in the sentiment analysis of arabic social media: a case study. In: Proceedings of the 9th International Conference on Innovations in Information Technology (IIT), pp. 215–220 (2013)
Soliman, T.H., Ali, M.M.: Mining social networks’ arabic slang comments. In: Proceedings of the IADIS European Conference on Data Mining (ECDM 2013), Prague, Czech Republic (2013)
Mourad, A., Darwish, K.: Subjectivity and sentiment analysis of modern standard arabic and arabic microblogs. In: Proceedings of 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Atlanta, Georgia, pp. 55–64 (2013)
McIlroy, S., Ali, N., Khalid, H., Hassan, A.E.: Analyzing and automatically labelling the types of user issues that are raised in mobile app reviews. In: Empirical Software Engineering, pp. 1067–1106. Springer (2016)
Carreno, L.V.G., Winbladh, K.: Analysis of user comments: an approach for software requirements evolution. In: Proceedings of the International Conference on Software Engineering (ICSE), San Francisco, CA, USA, pp. 582–591 (2013)
Goul, M., Marjanovic, O., Baxley, S., Vizecky, K.: Managing the enterprise business intelligence app store: sentiment analysis supported requirements engineering. In: Proceedings of the 45th Hawaii International Conference on System Sciences (2012)
Fu, B., Lin, J., Li, L., Faloutsos, C., Hong, J., Sadeh, N.: Why people hate your app: making sense of user feedback in a mobile app store. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, Illinois, USA (2013)
Iacob, C., Harrison, R.: Retrieving and analyzing mobile apps feature requests from online reviews. In: Proceedings of the International Working Conference on Mining Software Repositories (MSR), San Francisco, CA, USA (2013)
Jiang, W., Ruan, H., Zhang, L., Lew, P., Jiang, J.: For user-driven software evolution: requirements elicitation derived from mining online reviews. In: Proceedings of the 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan (2014)
Li, H., Zhang, L., Zhang, L., Shen, J.: A user satisfaction analysis approach for software evolution. In: Proceedings of the International Conference on Informatics and Computing (PIC) (2010)
Chen, N., Lin, J., Hoi, S., Xiao, X., Zhang, B.: AR-miner: mining informative reviews for developers from mobile app marketplace. In: Proceedings of the 36th International Conference on Software Engineering (ICSE), Hyderabad, India (2014)
Guzman, E., Bhuvanagiri, P., Bruegge, B.: FAVe: visualizing user feedback for software evolution. In: Proceedings of the Second IEEE Working Conference on Software Visualization (2014)
Sivagami, C., Punitha, S.C.: Ontology based sentiment clustering of movie review. Int. J. Emerg. Trends Technol. Comput. Sci. (IJETTCS) 2, 375–378 (2013)
Guizzardi, R., Morales-Ramirez, I., Perini, A.: A goal-oriented analysis to guide the development of a user feedback ontology. In: Proceedings of iStar. CEUR Workshop Proceedings (2014)
Badaro, G., Baly, R., Akel, R., Fayad, L., Khairallah, J., Hajj, H., et al.: A light lexicon-based mobile application for sentiment mining of arabic tweets. In: Proceedings of the Second Workshop on Arabic Natural Language Processing, Beijing, China (2015)
Al-Twairesh, N., Al-Khalifa, H., Al-Salman, A.: Subjectivity and sentiment analysis of arabic: trends and challenges. In: Proceedings of the 11th International Conference on Computer Systems and Applications (AICCSA) (2014)
Oraby, S., El-Sonbaty, Y., El-Nasr, M.A.: Finding opinion strength using rule-based parsing for arabic sentiment analysis. In: Advances in Soft Computing and Its Applications, pp. 509–520. Springer (2013)
ElSahar, H., El-Beltagy, S.R.: A fully automated approach for arabic slang lexicon extraction from microblogs. In: Computational Linguistics and Intelligent Text Processing, pp. 79–91. Springer (2014)
Al-Kabi, M.N., Gigieh, A.H., Alsmadi, I.M., Wahsheh, H.A., Haidar, M.M.: Opinion mining and analysis for Arabic language. Int. J. Adv. Comput. Sci. Appl. 5(5), 181–195 (2014)
Al-Aziz, A., Gheith, M., Ahmed, E.: Lexicon based and multi-criteria decision making (MCDM) approach for detecting emotions from Arabic microblog text. In: Proceedings of the 1st Arabic Computational Linguistics Conference in 16th International Conference, CICLing, Cairo, Egypt (2015)
Ibrahim, H., Abdou, S., Gheith, M.: MIKA: a tagged corpus for modem standard arabic and colloquial sentiment analysis. In: Proceedings of the 2nd International Conference on Recent Trends in Information Systems (ReTIS) (2015)
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Saudy, R.E., Nasr, E.S., El-Ghazaly, A.E.D.M., Gheith, M.H. (2018). Use of Arabic Sentiment Analysis for Mobile Applications’ Requirements Evolution: Trends and Challenges. In: Hassanien, A., Shaalan, K., Gaber, T., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. AISI 2017. Advances in Intelligent Systems and Computing, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-64861-3_45
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