Sentiment Prediction for Social Information Retrieval: A Comparative Study of Machine Learning and Deep Learning Approaches

DOI

10.2991/978-94-6463-496-9_2How to use a DOI?

Keywords

Sentiment analysis; social information retrieval; Comparative analysis; Machine learning; Deep learning

Abstract

Sentiment analysis plays a pivotal role in social information retrieval, enabling the extraction of valuable insights from user-generated content. In this study, we conduct a comprehensive comparative analysis of machine learning and deep learning approaches for sentiment prediction in the context of social media data, with a specific focus on the COVID-19 vaccine discourse. We investigate the performance of traditional machine learning classifiers, including Naive Bayes, Support Vector Machines, K-Nearest Neighbors, and Decision Tree, in conjunction with the TF-IDF representation model. In parallel, we assess the efficacy of deep learning models, such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid LSTM-CNN architecture, utilizing Word Embedding representation. Notably, the CNN model with Word2Vec vectorization demonstrates the highest performance. While the accuracy of the combined model, featuring the two LSTM-CNN classifiers, is slightly lower for our specific problem.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Aicha Boubekeur
AU  - Fouzia Benchikha
AU  - Naila Marir
PY  - 2024
DA  - 2024/08/31
TI  - Sentiment Prediction for Social Information Retrieval: A Comparative Study of Machine Learning and Deep Learning Approaches
BT  - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
PB  - Atlantis Press
SP  - 3
EP  - 18
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-496-9_2
DO  - 10.2991/978-94-6463-496-9_2
ID  - Boubekeur2024
ER  -