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An Effective Framework for Sentiment Analysis Using RNN and LSTM-Based Deep Learning Approaches

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Advances in Computing and Data Sciences (ICACDS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1848))

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Abstract

Sentiment Analysis (SA) is an active research field in modern times by studying people’s sentiments posted on different web platforms over the internet. To study these sentiments, different machine and deep learning techniques are in use. Deep learning is an efficient approach for SA. It gives more accurate results by learning text features from sentiments using a deep neural network. In this paper, we proposed a framework for sentiment classification;. The model was, trained, and tested on Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Deep Learning (DL) algorithms for SA. The performance of proposed framework was evaluated using accuracy, precision, recall, and F-1 score values. To calculate the performance, the data set was split in the ratio of 70:30, 80:20, and 90:10 for training and testing the model and observed that LSTM performs well with 88% accuracy and precision value, 87% recall value, and 87% F-1 measures.

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Authors and Affiliations

  1. Jaypee University of Engineering and Technology, A-B Road, Raghogarh, Distt. - Guna, M.P., India

    Brajesh Kumar Shrivash, Dinesh Kumar Verma & Prateek Pandey

Authors
  1. Brajesh Kumar Shrivash
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  2. Dinesh Kumar Verma
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  3. Prateek Pandey
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Corresponding author

Correspondence to Brajesh Kumar Shrivash .

Editor information

Editors and Affiliations

  1. Consilio Research Lab, Tallinn, Estonia

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, India

    P.K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

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Shrivash, B.K., Verma, D.K., Pandey, P. (2023). An Effective Framework for Sentiment Analysis Using RNN and LSTM-Based Deep Learning Approaches. In: Singh, M., Tyagi, V., Gupta, P., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2023. Communications in Computer and Information Science, vol 1848. Springer, Cham. https://doi.org/10.1007/978-3-031-37940-6_28

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  • DOI: https://doi.org/10.1007/978-3-031-37940-6_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37939-0

  • Online ISBN: 978-3-031-37940-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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