Evaluating few-shot learning strategies for extracting feature-sentiment pairs from app reviews
Organisatsiooni nimi
Software Engineering Analytics
Kokkuvõte
Automatically analyzing user reviews to understand sentiments toward app functionality (i.e., app features) is essential for aligning development efforts with user expectations and needs. Recent advances in Large Language Models (LLMs), such as ChatGPT, have demonstrated impressive performance across several tasks without needing parameter updates, using zero or few labeled examples. Despite these advancements, the effectiveness of LLMs in performing feature-specific sentiment analysis of user reviews with 0-, 1-, and 5-shot settings [1] remains limited. This thesis aims to evaluate various few-shot learning strategies for extracting feature-sentiment pairs from app reviews to enhance the performance of small-scale LLMs.
[1] Shah, F. A., Sabir, A., & Sharma, R. (2024). A Fine-grained Sentiment Analysis of App Reviews using Large Language Models: An Evaluation Study. arXiv preprint arXiv:2409.07162.
[1] Shah, F. A., Sabir, A., & Sharma, R. (2024). A Fine-grained Sentiment Analysis of App Reviews using Large Language Models: An Evaluation Study. arXiv preprint arXiv:2409.07162.
Lõputöö kaitsmise aasta
2024-2025
Juhendaja
Faiz Ali Shah and Ahmed Sabir
Suhtlemiskeel(ed)
inglise keel
Nõuded kandideerijale
Tase
Bakalaureus, Magister
Kandideerimise kontakt
Nimi
Faiz Ali Shah
Tel
E-mail