Transforming Sentiment Analysis Using Deep Learning Approaches: A Review

DOI

10.2991/978-94-6463-700-7_7How to use a DOI?

Keywords

Sentiment analysis (SA); Recurrent neural network (RNN); Deep neural network (DNN); Convolutional neural network (CNN); Recursive neural network (RNN); Deep belief network (DBN)

Abstract

In the age of digital transformation, vast amounts of textual data are generated every second across social media, e-commerce platforms, and other online forums, making the recognition and interpretation of sentiments within this data increasingly critical. The goal of SA (Sentiment Analysis), a subfield of NLP (Natural Language Processing), is to find and extract subjective information from written material, with uses spanning from brand tracking to forecasting stock market trends. Traditional methodologies, which rely on lexicons and statistical models, have demonstrated limited scalability and adaptability when dealing with the complexities of human communication. Deep learning has revolutionized sentiment analysis by developing algorithms capable of detecting complex language patterns and contextual interactions. Models such as CNNs (Convolutional Neural Networks), RNNs (Recurrent Neural Networks), and advanced designs like Transformer-Based Bidirectional Encoder Representations BERT have transformed the field due to their remarkable accuracy and efficiency. This study explores the evolution of sentiment analysis techniques, focusing on the transformative power of deep learning models. It analyzes their applications across a wide range of datasets, showing their advantages over traditional techniques while also critically assessing the challenges they encounter, including as processing requirements and interpretability difficulties. This study aims to provide an extensive review of deep learning-based sentiment analysis DL-SA, identify research gaps, and suggest future avenues for exploration. For researchers and professionals hoping to use DL (Deep Learning) for SA (Sentiment Analysis) in a world that is becoming more and more data-driven, this paper is a great resource.

Copyright

© 2025 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  - Ronit Raj
AU  - Navneet Kaur
AU  - Paurav Goel
PY  - 2025
DA  - 2025/04/19
TI  - Transforming Sentiment Analysis Using Deep Learning Approaches: A Review
BT  - Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
PB  - Atlantis Press
SP  - 63
EP  - 79
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-700-7_7
DO  - 10.2991/978-94-6463-700-7_7
ID  - Raj2025
ER  -