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

Pallavi Shukla, Rakesh Kumar, Vijay Kumar Dwivedi and Ashutosh Kumar Singh

International Journal of Data Mining, Modelling and Management, 2026, vol. 18, issue 2, 180-214

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. (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.inderscience.com/link.php?id=154483 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:18:y:2026:i:2:p:180-214

Access Statistics for this article

More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().