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@article{198212,
        author = {Pathan zaki and Kamini Sharma},
        title = {EMOTION DETECTION FROM TEXT USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {13554-13562},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198212},
        abstract = {The advancement of Natural Language Processing (NLP) and machine learning has dramatically changed the field of emotion detection from textual data. With the use of intelligent text classification algorithms and the optimization of emotion recognition, the concept of automated emotion analysis has become more prevalent. However, the development of effective emotion detection systems from scratch requires significant data and computation. This research aims to explore the use of emotion detection using machine learning algorithms for the prediction and classification of hu- man emotions from text. Six machine learning algorithms were implemented and compared using Multinomial Naive Bayes, Logistic Regression, Linear SVM, Random Forest, XGBoost, and Multi-Layer Perceptron models. The proposed architecture was trained using an extensive dataset containing labeled emotional text data. The experimental results showed that the use of these algorithms significantly improves emotion detection accuracy. The proposed architecture using the Linear SVM model showed the most promising results. The proposed architecture was able to attain an ac- curacy of 89.97% in the prediction of emotions from textual input.},
        keywords = {Emotion Detection, Natural Language Processing, Machine Learning, Linear SVM, Text Classification, TF-IDF},
        month = {April},
        }