Predicting Emotions from Twitter Posts: A Comparative Study of Machine Learning Methods

DOI

10.2991/978-94-6463-300-9_13How to use a DOI?

Keywords

emotion prediction; sentiment analysis; random forest; Multinomial Naive Bayes; SVM

Abstract

With the increasing importance of social media platforms such as Twitter, understanding the emotions expressed in text data has become crucial for various applications. Manual analysis of the vast amount of user-generated content is impractical, highlighting the need for automated classification techniques. This study focuses on evaluating different machine learning methods for predicting emotions from Twitter posts, specifically examining Multinomial Naive Bayes (MultinomiaNB), Support Vector Machines (SVM), and the Random Forest. A dataset containing over 4000 labeled tweets, categorized as positive, neutral, or negative, is used for evaluation purposes. The challenges associated with predicting emotions from Twitter text, including natural language ambiguity and noise, are carefully considered. The results demonstrate that all models perform well, with SVM exhibiting a slight advantage. This study contributes to a deeper understanding of user emotions and public opinion in social media contexts. Future research directions include refining preprocessing techniques, exploring advanced methods like deep learning, incorporating additional features, and leveraging ensemble learning approaches in order for higher accuracy.

Copyright

© 2023 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.

Download article (PDF)

TY  - CONF
AU  - Peihang Li
PY  - 2023
DA  - 2023/11/27
TI  - Predicting Emotions from Twitter Posts: A Comparative Study of Machine Learning Methods
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 122
EP  - 129
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_13
DO  - 10.2991/978-94-6463-300-9_13
ID  - Li2023
ER  -