Sentiment Analysis Using Support Vector Machines, Neural Networks, and Random Forests

DOI

10.2991/978-94-6463-300-9_4How to use a DOI?

Keywords

Sentiment Analysis; Support Vector Machines; Neural Networks

Abstract

Sentiment analysis, often referred to as opinion mining, plays a crucial role in the field of natural language processing by computationally analyzing text to extract sentiments or opinions. It has become essential in understanding public sentiment, evaluating customer feedback, and identifying market trends, particularly in the age of online platforms and social media. The background of sentiment analysis stems from the need to process vast amounts of textual data and extract meaningful sentiment information. Manual analysis is impractical due to the exponential growth of online content, prompting the development of automated sentiment analysis techniques. Sentiment analysis holds significant value in uncovering insights related to customer satisfaction, brand perception, and sentiment trends. It empowers businesses to improve strategies, enhance customer experiences, and researchers to understand public opinion on various issues. Government agencies can also benefit from assessing public sentiment for evidence-based decision-making. This paper provides an overview of three prominent sub-topics in sentiment analysis: Support Vector Machines (SVMs), Neural Networks, and Random Forests. These approaches have gained significant attention in both research and industry applications, contributing to sentiment classification advancements.

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  - Colin Kai Wang
PY  - 2023
DA  - 2023/11/27
TI  - Sentiment Analysis Using Support Vector Machines, Neural Networks, and Random Forests
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 23
EP  - 34
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_4
DO  - 10.2991/978-94-6463-300-9_4
ID  - Wang2023
ER  -