Authors : MD Tahseen Equbal; Mohammad Mamoon; Mohammad Sajid Khan; Padmani Yadav

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/3a8mzzkd

Scribd : https://tinyurl.com/bh8wst4r

DOI : https://doi.org/10.38124/ijisrt/26jun444

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Understanding human emotions from textual data has become increasingly important in various applications such as social media monitoring, customer feedback analysis, and intelligent conversational systems. This paper presents an Emotion-Aware Text Analytics framework that utilizes Natural Language Processing (NLP) techniques and machine learning algorithms to classify textual content into multiple emotion categories, including Happy, Sad, Angry, Fear, Surprise, and Neutral. The proposed approach incorporates data preprocessing, TF-IDF feature extraction, and emotion classification to improve sentiment understanding. Experimental results demonstrate the effectiveness of the framework, achieving an accuracy of 92.4% and an AUC score of 0.96. The findings indicate that the proposed system provides an efficient and reliable solution for automated emotion recognition from textual data.

Keywords : Emotion Detection, Sentiment Analysis, Natural Language Processing, Text Analytics, Machine Learning, Emotion Classification.

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