Title: ATESA: audio text emotion and sentiment analyser - a sentiment and emotion analysis tool based on deep learning methods

Authors: Pallavi Shukla; Rakesh Kumar; Vijay Kumar Dwivedi; Ashutosh Kumar Singh

Addresses: Department of Computer Science and Engineering, Rabindra Nath Tagore University, Raisen, 464551, MP, India ' Department of Computer Science and Engineering, Rabindra Nath Tagore University, Raisen, 464551, MP, India ' Department of Computer Science and Engineering, Rabindra Nath Tagore University, Raisen, 464551, MP, India; Department of Computer Science and Engineering, United College of Engineering and Research, Naini, Prayagraj, 211010, UP, India ' Department of Computer Science, Allahabad Degree College, University of Allahabad, Kydganj, Prayagraj, 211003, UP, India

Abstract: Sentiment analysis (SA) identifies sentiments in text, reviews, tweets, audio, images, and videos. Sentiment integrates emotion and thinking, with emotions being temporary while sentiments last longer. Emotion recognition and sentiment polarity analysis are gaining popularity in natural language processing due to their ability to mine social media data. This study applies machine learning (ML) classifiers such as random forest, logistic regression, support vector machine, and decision tree to classify text and speech as positive, negative, or neutral. Additionally, it explores available sentiment analysis tools and introduces the audio text emotion and sentiment analyser (ATESA). ATESA leverages ensemble-oriented classification techniques using deep learning, specifically bidirectional long-short-term memory recurrent neural networks (Bi-LSTM-RNN). It processes text, Twitter data, and speech converted into text. Experimental results show that ATESA achieves 92% accuracy, outperforming other algorithms.

Keywords: sentiment analysis tool; bidirectional long-short-term memory; Bi-LSTM; recurrent neural network; RNN; TFIDF; deep learning.

DOI: 10.1504/IJDMMM.2026.154483

International Journal of Data Mining, Modelling and Management, 2026 Vol.18 No.2, pp.180 - 214

Received: 26 Aug 2024
Accepted: 06 Mar 2025
Published online: 30 Jun 2026 *

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