Applying transfer learning to sentiment analysis in social media
Visualitza/Obre
10.1109/REW53955.2021.00060
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/361570
Tipus de documentText en actes de congrés
Data publicació2021
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
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Abstract
Context: Sentiment analysis is an NLP technique that can be used to automatically obtain the sentiment of a crowd of end-users regarding a software application. However, applying sentiment analysis is a difficult task, especially considering the need of obtaining enough good quality data for training a Machine Learning (ML) model. To address this challenge, transfer learning can help us save time and get better performance results with a limited amount of data. Objective: In this paper, we aim at identifying to which degree transfer learning improves the results of sentiment analysis of messages shared by end-users in social media. Method: We propose a tool-supported framework able to monitor and analyze the sentiment of tweets with different ML models and settings. Using the proposed framework, we apply transfer learning and conduct a set of experiments with multiple datasets. Results: The performance of different ML models with transfer learning from different datasets are obtained and discussed, showing how different factors affect the results, and discussing how they have to be considered when applying transfer learning.
CitacióDe Arriba, A.; Oriol, M.; Franch, X. Applying transfer learning to sentiment analysis in social media. A: International Workshop on Crowd-Based Requirements Engineering. "29th IEEE International Requirements Engineering Conference Workshops, REW 2021: September 20–24 2021, online event: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 342-348. ISBN 978-1-6654-1898-0. DOI 10.1109/REW53955.2021.00060.
ISBN978-1-6654-1898-0
Versió de l'editorhttps://ieeexplore.ieee.org/document/9582287
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