RE-Miner 2.0: a holistic framework for mining mobile application reviews
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| Title: | RE-Miner 2.0: a holistic framework for mining mobile application reviews |
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| Authors: | Tiessler Aguirre, Max, Motger de la Encarnación, Joaquim |
| Source: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| Publisher Information: | CEUR-WS.org, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | Feature clustering, Àrees temàtiques de la UPC::Informàtica::Enginyeria del software, Type classification, Natural language processing, Topic classification, Emotion classification, Mobile app reviews, Feature extraction, Polarity analysis, Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural |
| Description: | In the domain of application stores and marketplaces, user reviews are crucial for supporting multiple requirements engineering tasks. Feature extraction, emotion classification, topic analysis, review type identification, and polarity analysis are key components in requirements prioritization, feedback gathering, and release planning. Empirical evaluation of these techniques is challenging due to data collection complexities and a lack of reproducible methods and available tools. Furthermore, existing studies often focus on isolated tasks, hindering a comprehensive analysis of user perceptions. This paper introduces RE-Miner 2.0, a work-in-progress tool that integrates multiple data extraction and analysis methods in a distributed environment (RE-Miner Ecosystem), enabling a multidimensional and detailed analysis of user feedback. It offers a web-based service for task integration and comparison, supported by persistent storage and a web application that allows analytical visualization of reviews. As a result, RE-Miner 2.0 provides a platform for task integration, replication, and comparison of review mining techniques. Bringing advancements in deep review analysis for requirements engineering. A demo of the tool is showcased here: https://www.youtube.com/watch?v=a11bHSCYqqM. With the support from the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund. This paper has been funded by the Spanish Ministerio de Ciencia e Innovación under project / funding scheme PID2020-117191RB-I00 / AEI/10.13039/501100011033. |
| Document Type: | Conference object |
| File Description: | application/pdf |
| Language: | English |
| Access URL: | https://hdl.handle.net/2117/432917 |
| Rights: | CC BY |
| Accession Number: | edsair.dedup.wf.002..5de68e5e7944b886c26c91891e7d1d54 |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | In the domain of application stores and marketplaces, user reviews are crucial for supporting multiple requirements engineering tasks. Feature extraction, emotion classification, topic analysis, review type identification, and polarity analysis are key components in requirements prioritization, feedback gathering, and release planning. Empirical evaluation of these techniques is challenging due to data collection complexities and a lack of reproducible methods and available tools. Furthermore, existing studies often focus on isolated tasks, hindering a comprehensive analysis of user perceptions. This paper introduces RE-Miner 2.0, a work-in-progress tool that integrates multiple data extraction and analysis methods in a distributed environment (RE-Miner Ecosystem), enabling a multidimensional and detailed analysis of user feedback. It offers a web-based service for task integration and comparison, supported by persistent storage and a web application that allows analytical visualization of reviews. As a result, RE-Miner 2.0 provides a platform for task integration, replication, and comparison of review mining techniques. Bringing advancements in deep review analysis for requirements engineering. A demo of the tool is showcased here: https://www.youtube.com/watch?v=a11bHSCYqqM.<br />With the support from the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund. This paper has been funded by the Spanish Ministerio de Ciencia e Innovación under project / funding scheme PID2020-117191RB-I00 / AEI/10.13039/501100011033. |
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