Abstract
Analyzing the sentiment behind emojis can provide valuable insights into the emotional context and user sentiment associated with textual content. To conduct a comparative analysis of diverse supervised machine learning models that can achieve the highest level of accuracy in Emoji Sentiment Analysis is the purpose of this research. Five machine learning models used in this research are K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Logistic Regression, Naive Bayes, and Random Forest. The experimental process resulted in ANN and KNN models giving an accuracy of 92%. The ANN model shows its proficiency in effectively managing large datasets. ANN also supports fault tolerance. The KNN model refrains from conducting calculations during the training phase and only constructs a model when a query is executed on the dataset. This characteristic makes KNN particularly well-suited for data mining. Both ANN and K-NN excelled in the experimental study due to these distinctive attributes.
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Acknowledgements
The authors are indebted to the Department of CSE at CHRIST (Deemed to be University), Bengaluru, India, for the invaluable infrastructure that was provided and for the technical support.
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Swetha Cordelia, A., Kokatnoor, S.A. (2024). Emoji Sentiment Analysis of User Reviews on Online Applications Using Supervised Machine Learning. In: Shukla, S., Sayama, H., Kureethara, J.V., Mishra, D.K. (eds) Data Science and Security. IDSCS 2023. Lecture Notes in Networks and Systems, vol 922. Springer, Singapore. https://doi.org/10.1007/978-981-97-0975-5_23
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