Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India

Research Article

Emotion Detection and Recommender System using Machine Learning

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  • @INPROCEEDINGS{10.4108/eai.23-11-2023.2343260,
        author={Alen Wenish J and Satishkumar K and Kishore R and Anitha Julian},
        title={Emotion Detection and Recommender System using Machine Learning},
        proceedings={Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India},
        publisher={EAI},
        proceedings_a={IACIDS},
        year={2024},
        month={3},
        keywords={emotion detection machine learning recommendation system emotion analysis},
        doi={10.4108/eai.23-11-2023.2343260}
    }
    
  • Alen Wenish J
    Satishkumar K
    Kishore R
    Anitha Julian
    Year: 2024
    Emotion Detection and Recommender System using Machine Learning
    IACIDS
    EAI
    DOI: 10.4108/eai.23-11-2023.2343260
Alen Wenish J1,*, Satishkumar K1, Kishore R2, Anitha Julian3
  • 1: EmbedUR Systems, Chennai, Tamil Nadu, India
  • 2: Light & Wonder, Chennai, Tamil Nadu, India
  • 3: Saveetha Engineering College, Chennai, India
*Contact email: alenwenish@gmail.com

Abstract

With the expansion of online community, textual data has emerged as the primary medium for human-machine and human-human interaction. Recognizing emotions existing in the communication or emotions of the engaged users to enhance the user experience is one such crucial foundation in humanizing such interaction. Humans are capable of expressing their feelings through speech, writing, and facial expressions. The endeavor of identifying emotions in text texts is primarily a classification problem that incorporates principles from machine learning and natural language processing. In applications like chatbots and customer service forums identifying emotions from a text authored by a human is crucial. In our proposed method, we used web development, where text was accepted from the user through website, and machine learning was used to classify the text supplied to determine emotion. The system suggests websites, video links, motivational quotations, etc. based on emotion that the machine learning algorithm has identified.