Abstract
The detection and analysis of sentiment have received great importance by professionals from ecommerce, medical services, physiologists, and social critics. The computerization has resulted in tremendous increase of online users. The users have adapted posting their day to day life events, expressions, judgement, memories, etc. on social media platforms like Facebook, WhatsApp, and Twitter as a part of life. Online postings consist of text and huge number of emojis as well. To evaluate the correct sentiment of such online postings, an accurate approach is required which evaluates the score and polarity of emoji used in an online sentiment. In the current paper, we have proposed an approach to evaluate score and polarity of 4158 emojis. Emoji evaluation is a task of determining the score and polarity of emoji as positive, negative, and neutral based on the sentiment of sentence and the short name description of emoji. The accuracy of the proposed approach is evaluated through machine learning approaches like Linear Regression, SVM and Random Forest. Our approach outperforms in SVM with an accuracy of 85%.
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Gupta, S., Singh, A., Ranjan, J. (2021). Emoji Score and Polarity Evaluation Using CLDR Short Name and Expression Sentiment. In: Abraham, A., et al. Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020). SoCPaR 2020. Advances in Intelligent Systems and Computing, vol 1383. Springer, Cham. https://doi.org/10.1007/978-3-030-73689-7_95
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DOI: https://doi.org/10.1007/978-3-030-73689-7_95
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