Enhancing Sentiment Analysis Performance Using SMOTE and Majority Voting in Machine Learning Algorithms

DOI

10.2991/978-94-6463-620-8_10How to use a DOI?

Keywords

Machine Learning; Majority Voting; SMOTE; Sentiment Analysis

Abstract

In the digital era, sentiment analysis on social media has become increasingly important in understanding public perception of various issues. However, one of the main challenges in sentiment analysis is the issue of data imbalance, where one class (such as positive sentiment) may significantly outnumber another (such as negative or neutral sentiment). This imbalance can lead to biased predictions in machine learning models, where the majority class is favored over the minority class. To address this, Synthetic Minority Over-sampling Technique (SMOTE) is used to artificially balance the dataset by creating synthetic samples from the minority class. SMOTE generates new instances by interpolating between existing minority instances, improving the distribution of the data and enhancing model performance. In this research, various machine learning algorithms are utilized to perform sentiment analysis on tweets collected with the hashtag “online learning”. The SMOTE oversampling technique is applied and compared with models that do not use SMOTE. This research focuses mainly on the Majority Voting algorithm, which combines predictions from multiple models to improve overall accuracy. The test results show that using SMOTE significantly improves the model’s performance, especially in terms of recall and F1-Score. The Majority Voting+SMOTE algorithm achieved the highest accuracy of 97%, demonstrating the effectiveness of this approach in handling data imbalance and producing more reliable predictions. These results confirm that SMOTE effectively improves model performance under imbalanced data conditions, especially in sentiment analysis.

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  - Fadli Suandi
AU  - M. Khairul Anam
AU  - Muhammad Bambang Firdaus
AU  - Sofiansyah Fadli
AU  - Lathifah Lathifah
AU  - Eva Yumami
AU  - Alfa Saleh
AU  - Ade Zulkarnain Hasibuan
PY  - 2024
DA  - 2024/12/25
TI  - Enhancing Sentiment Analysis Performance Using SMOTE and Majority Voting in Machine Learning Algorithms
BT  - Proceedings of the  7th International Conference on Applied Engineering (ICAE 2024)
PB  - Atlantis Press
SP  - 126
EP  - 138
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-620-8_10
DO  - 10.2991/978-94-6463-620-8_10
ID  - Suandi2024
ER  -