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@article{205263,
        author = {Shashwat Rai and Gaurav Singh and Mrinmoy Kayal},
        title = {Text-Based Emotion Recognition Using Supervised Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {5899-5905},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205263},
        abstract = {A supervised machine learning model that identifies human emotions in text, such as reviews, chat, comments, social media posts etc. Is referred to as Text Based Emotion Recognition using supervised machine learning. The system uses machine learning algorithms like Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR) to identify various emotions such as happy, sad, anger, fear, surprise and neutral expression. The proposed research was evaluated by a thorough experiment with the standard metrics, namely accuracy, precision, recall and f1-score to analyze and determine which machine learning model perform better on text emotion classification task. This proposed model was trained using a dataset with 416,809 text records in which the result showed that logistic regression outperformed the other ma-chine learning models in terms of accuracy, thus predicting and classifying text emotionally. There are also pre-processing natural language processing techniques such as tokenization, stop-words removal, stemming, and also feature extraction techniques like TF-IDF, Bag-of-Words which were included to perform better classification. The achieved results demonstrate the effective performance of these advanced supervised machine learning models in detecting human emotions in text with minimized manual interference for emotional analysis, can revolutionize human-computer interaction and would be greatly beneficial in fields like mental health monitoring, analysis of customer feedback, chatbots, recommendation engines, learning platforms, social media monitoring etc.},
        keywords = {component, formatting, style, styling, insert},
        month = {June},
        }