Mètriques per a la detecció d'esdeveniments en app reviews
User reviews in app stores contain valuable user feedback useful for app developers and stakeholders. However, it is impractical to manually read this high-volume data as popular apps can receive thousands of reviews per day. This master's thesis proposes a machine learning pipeline for automat...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/411655 |
| Acceso en línea: | https://hdl.handle.net/2117/411655 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Mobile apps aplicaciones móviles análisis de mercado aprendizaje automático reseñas de usuarios detección de eventos microblogging mobile apps market analysis machine learning user reviews event detection Aprenentatge automàtic Aplicacions mòbils Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació |
| Sumario: | User reviews in app stores contain valuable user feedback useful for app developers and stakeholders. However, it is impractical to manually read this high-volume data as popular apps can receive thousands of reviews per day. This master's thesis proposes a machine learning pipeline for automatically detecting significant software-based events from user reviews of mobile apps. The pipeline involves preprocessing raw reviews, aggregating data weekly, extracting relevant metrics like review count, rating, polarity, and count and percentage of negative, neutral and positive reviews, as well as training machine learning classifiers on two manually annotated truth sets to identify events from 10 microblogging Android apps spanning one year. Each model was trained on two truth sets: reviews of all ratings and positively-rated reviews only. Out of four trained models: Multilayer Perceptron, Naive Bayes, Support Vector Machines and Random Forest, the Multilayer Perceptron and Random Forest models achieved high F1 scores of 97\% and 93\% on the all ratings truth set and 53\% and 87\% on the positive only truth set respectively. Through qualitative assessment via summarization, the detected events provided actionable insights for app developers about bugs, performance issues, feature requests, and user feedback. The findings demonstrate the effectiveness of leveraging machine learning techniques for extracting valuable information from large volumes of app reviews to support development efforts. |
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