Abstract
Sarcasm is defined a form of irony that a user uses to express its feeling via social networking platforms such as Twitter or Facebook. Sarcasm can be used in different situations such as mockery or constructive criticism. Verbal sarcasm is rather easy to determine, but detecting sarcasm in the written text is equally difficult. Determining sarcasm in the text improves the sentiment analysis process and also the decision-making process. Sentiment analysis refers to the processing of determining the negative and positive emotions of the Internet user over a specific topic. The dataset used in this research is collected from Twitter via Web scrapping. Four machine learning algorithms such as linear SVC (accuracy = 83%, f1-score = 0.81), Naïve Bayes (accuracy = 74%, f1-score = 0.73), logistic regression (accuracy = 83%, f1-score = 0.81) and random forest classifier (accuracy = 80%, f1-score = 0.77) were implemented.
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Chaudhary, S., Kakkar, M. (2021). Sarcasm Detection Technique on Twitter Data with Natural Language Processing. In: Tiwari, S., Suryani, E., Ng, A.K., Mishra, K.K., Singh, N. (eds) Proceedings of International Conference on Big Data, Machine Learning and their Applications. Lecture Notes in Networks and Systems, vol 150. Springer, Singapore. https://doi.org/10.1007/978-981-15-8377-3_24
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DOI: https://doi.org/10.1007/978-981-15-8377-3_24
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