A Critical Review on Sentiment Analysis Based on Deep Learning Techniques

Author(s)	Ankit Kumar, Nitesh Gupta, Anurag Shrivastava
Country	India
Abstract	Sentiment analysis, a vital task in natural language processing, has evolved significantly with the adoption of deep learning techniques. This review critically examines the current state of sentiment analysis based on deep learning methods, focusing on their performance, scalability, and challenges. We explore key deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), Long Short-Term Memory (LSTM), and attention mechanisms. These models have shown remarkable improvements in sentiment prediction accuracy compared to traditional machine learning approaches. However, issues like data scarcity, interpretability, and computational complexity remain challenging. This review provides insights into existing solutions, evaluates emerging trends, and outlines future directions to enhance deep learning applications in sentiment analysis across diverse domains.
Keywords	Sentiment Analysis, Hybrid model, CNN, RNN, Deep Learning
Field	Engineering
Published In	Volume 6, Issue 5, September-October 2024
Published On	2024-10-10
Cite This	A Critical Review on Sentiment Analysis Based on Deep Learning Techniques - Ankit Kumar, Nitesh Gupta, Anurag Shrivastava - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.28572
DOI	https://doi.org/10.36948/ijfmr.2024.v06i05.28572
Short DOI	https://doi.org/g7942c

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International Journal For Multidisciplinary Research