Abstract
The analysis of the textual content of user opinions on social networks about software applications in use can provide valuable information to the development and support teams, in terms of errors, dissatisfactions, new functional requirements, among others. The paper presents a solution based on intelligent technologies to automatically classify whether or not the content of a review is relevant to a software support team. This solution combines machine learning algorithms, with the use of a domain-specific glossary for feature selection, in predicting the relevance of reviews. The proposed solution was evaluated experimentally with three datasets, specifically Facebook, Tapfish and SwiftKey, and the results obtained were very promising.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdulmunim, A., Younis, Y.S.: Supporting classification of software requirements system using intelligent technologies algorithms. Technium 3(11), 32–39 (2021)
Araujo Arredondo, N.P.: Método Semisupervisado para la Clasificación Automática de Textos de Opinión. Tesis de Maestría en Ciencias Computacionales, INAOE (2009)
Bengfort, B., Bilbro, R., Ojeda, T.: Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning. O’Reilly Media (2018)
Bird, S.: Nltk: the natural language toolkit. In: Proceedings of the COLING/ACL on Interactive Presentation Sessions,pp. 69–72 (2006)
Casamayor, A., Godoy, D., Campo, M.: Identification of non-functional requirements in textual specifications: a semi-supervised learning approach. Inf. Softw. Technol. 52(4), 436–445 (2010)
Chen, N., Lin, J., Hoi, S.C., 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, pp. 767–778. Hyderabad, India (2014)
Ciurumelea, A., Schaufelbühl, A., Panichella, S., Gall, H.C.: Analyzing reviews and code of mobile apps for better release planning. In: 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 91–102. Klagenfurt, Austria (2017)
Dias Canedo, E., Cordeiro Mendes, B.: Software requirements classification using machine learning algorithms. Entropy 1057 22(9), 1–20 (2020)
Foundation, P.S.: Python 3.11.1 documentation, April (2023)
González Guerra, P.S.: Hybrid method of emotion detection with semantic approach in feature selection (2021)
Groen, E.C., Doerr, J., Adam, S.: Towards crowd-based requirements engineering a research preview. In: Requirements Engineering: Foundation for Software Quality: 21st International Working Conference, REFSQ 2015, pp. 247–253. Essen, Germany (2015)
Hao, J., Ho, T.K.: Machine learning made easy: a review of scikit-learn package in python programming language. J. Educ. Behav. Stat. 44(3), 348–361 (2019)
ISO/IEC/IEEE. International standard. Systems and software engineering — vocabulary (2017)
Jindal, R.R., Malhotra, R., Jain, A.: Automated classification of security requirements. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 2027–2033. Jaipur, India, (2016)
Kurtanovićand, Z., Maalej, W.: Automatically classifying functional and non-functional requirements using supervised machine learning. In: 2017 IEEE 25th International Requirements Engineering Conference (RE), pp. 490–495. Lisbon, Portugal (2017)
Maalej, W., Nabil, H.: Bug report, feature request, or simply praise? on automatically classifying app review. In: 2015 IEEE 23rd International Requirements Engineering Conference (RE), pp. 116–125. Ottawa, Canada (2015)
Maalej, W., Nayebi, M., Johann, T., Ruhe, G.: Toward data-driven requirements engineering. IEEE Softw. 33(1), 48–54 (2015)
Malhotra, R., Khanna, M.: An exploratory study for software change prediction in object oriented systems using hybridized techniques. Autom. Softw. Eng. 24, 673–717 (2017)
Milián Núñez, V., González Diez, H., Simón Cuevas, A.: Predicting the evolution of software requirements based on user reviews. In: 6th International Conference on Computer Science (CICCI’ 2022), Proceedings of 18th International Convention and Fair INFORMATICA 2022, Havana, Cuba (2022)
Nagpalnjm, M.D., Kalia, A.: A comprehensive analysis of requirement engineering utilizing machine learning techniques. Design Engineering 2662–2678 (2021)
Ott, D.: Automatic requirement categorization of large natural language specifications at mercedes-benz for review improvements”. In: Requirements Engineering: Foundation for Software Quality: 19th International Working Conference, REFSQ 2013, pp. 50–64. Essen, Germany (2013)
Pang, Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05), pp. 115–124 (2005)
Villarroel, L., Bavota Russo, G.B., Oliveto, R., di Penta, M.: Release planning of mobile apps based on user reviews. In: Proceedings of the 38th International Conference on Software Engineering, pp. 14–24. Austin, USA (2016)
Villena Román, J., Collada Pérez, S., Lana Serrano, S., González Cristóbal, J.C.: Hybrid method for categorization of text based on learning and rules. Nat. Lang. Process. 46, 35–43 (2011)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Milián Núñez, V., Blanco Martín, T., Simón-Cuevas, A., González Diéz, H., Hernández González, A. (2024). A Knowledge-Based User Feedback Classification Approach for Software Support. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2023. Lecture Notes in Computer Science, vol 14335. Springer, Cham. https://doi.org/10.1007/978-3-031-49552-6_21
Download citation
DOI: https://doi.org/10.1007/978-3-031-49552-6_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49551-9
Online ISBN: 978-3-031-49552-6
eBook Packages: Computer ScienceComputer Science (R0)