Applying transfer learning to sentiment analysis in social media
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
ProjectDESARROLLO, OPERATIVA Y GOBERNANZA DE DATOS PARA SISTEMAS SOFTWARE BASADOS EN APRENDIZAJE AUTOMATICO (AEI-PID2020-117191RB-I00)
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.
CitationDe 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.