Deep Learning-based Sentiment Analysis of Text using Long Short-Term Memory Networks

MD Shahid Ali; Saif Ali; Abdullah Parwez; Abu Sufiyan; Mohd Haroon

doi:10.55524/ijircst.2025.13.3.22

Abstract

Big texts data are tiresome to sift through manually. Sentiment analysis is a machine process employing calculating (AI) to determine positive and negative sentiment from the text. Sentiment analysis is most frequently utilized in gathering insights through social media messages, survey answers, and customer opinions to make data-informed decisions. Sentiment analysis tools are highly rated to contribute to the unstructured text in terms of business process automation and hours saved in manual processing. Deep Learning (DL) has achieved unprecedented spotlight for industry and academia during the recent past for their excellent performance on an unprecedented range of applications. Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are the most universal types of DL architecture utilized in current applications. We use LSTM for sentiment analysis of textual commentaries. Recent years, however, have made neural networks especially successful at sentiment classification due to their ability to process large sets of information. Especially long STM networks.

Keywords

Sentiment Analysis, Text Classification, LSTM, Deep Learning.

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Cites this article as

M. S. Ali, S. A. , A. Parwez, A. Sufiyan, M. Haroon, "Deep Learning-based Sentiment Analysis of Text using Long Short-Term Memory Networks", International Journal of Innovative Research in Engineering & Management (ijircst), Vol-13, Issue-3, Page No-142-148, 2025. Available from: https://doi.org/10.55524/ijircst.2025.13.3.22

Corresponding Author

MD Shahid Ali

B.Tech Scholar, Department of Computer Science and Engineering, Integral University, Lucknow, India

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