A Comparative Study of Hybrid Deep Sentimental Analysis Learning Techniques with CNN and SVM

Juyal, Prachi; Kundaliya, Amit

doi:10.1109/aic57670.2023.10263883

2023 IEEE World Conference on Applied Intelligence and Computing (AIC) 2023

DOI: 10.1109/aic57670.2023.10263883

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A Comparative Study of Hybrid Deep Sentimental Analysis Learning Techniques with CNN and SVM

Prachi Juyal,

Amit Kundaliya

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Sentimental Analysis using Neural Network

Vishal Satpute,

Vaibhav Singh,

Dhananjay Gayke

et al. 2023

IJARSCT

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This paper presents a comprehensive exploration of sentiment analysis using neural networks, focusing on architectures like RNNs, LSTMs, and transformers. Key findings include an in-depth analysis of model effectiveness, challenges in training data, and ethical considerations. The survey showcases the prominence of RNNs in capturing nuanced sentiment patterns, offering valuable insights for researchers and industry practitioners. Methodologically, dataset curation, preprocessing, and ethical considerations are discussed, culminating in a systematic approach to sentiment analysis. The model's applications span customer feedback, brand monitoring, market research, political sentiment analysis, healthcare feedback, content moderation, financial sentiment analysis, and educational feedback analysis. The paper concludes with a significant contribution to the evolving landscape of sentiment analysis, emphasizing the integration of neural networks for a deeper understanding of human sentiment

show abstract

Sentimental Analysis using Neural Network

Vishal Satpute,

Vaibhav Singh,

Dhananjay Gayke

et al. 2023

IJARSCT

View full text Add to dashboard Cite

show abstract

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A Comparative Study of Hybrid Deep Sentimental Analysis Learning Techniques with CNN and SVM

Cited by 1 publication

References 18 publications

Sentimental Analysis using Neural Network

Sentimental Analysis using Neural Network

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