Sentiment analysis with machine learning and deep learning: A survey of techniques and applications

Nikhil Sanjay Suryawanshi *

California, USA.
 
Review
International Journal of Science and Research Archive, 2024, 12(02), 005–015.
Article DOI: 10.30574/ijsra.2024.12.2.1205
Publication history: 
Received on 19 May 2024; revised on 26 June 2024; accepted on 29 June 2024
 
Abstract: 
Sentiment analysis is the task of automatically identifying the sentiment expressed in text. It has become increasingly important in many applications such as social media monitoring, product reviews analysis, and customer feedback evaluation. With the advent of deep learning techniques, sentiment analysis has seen significant improvements in performance and accuracy. This paper presents a comprehensive survey of machine learning and deep learning methods for sentiment analysis at the document, sentence, and aspect levels. We first provide an overview of traditional machine learning approaches to sentiment analysis and their limitations. We then look into various machine learning and deep learning architectures that have been successfully applied to this task. Additionally, we discuss the challenges of dealing with different data modalities, such as visual and multimodal data, and how both techniques have been adapted to address these challenges. Furthermore, we explore the applications of sentiment analysis in diverse domains, including social media, product reviews, and healthcare. Finally, we highlight the current limitations of deep learning approaches for sentiment analysis and outline potential future research directions. This survey aims to provide researchers and practitioners with a comprehensive understanding of the state-of-the-art deep learning techniques for sentiment analysis and their practical applications.
 
Keywords: 
Natural Language Processing; Sentiment Analysis; Text Analysis; Recurrent Neural Network; Deep Neural Network; Convolutional Neural Network; Machine Learning; Deep Learning
 
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0