Textual Data Analysis for Identifying Sarcasm in Kannada

Abstract

Sarcasm, a form of verbal irony, presents a significant challenge in natural language processing due to its context-dependent and often subtle nature. This research focuses on the identification of sarcasm in Kannada, a Dravidian language spoken predominantly in the Indian state of Karnataka. The study employs a computational approach, leveraging textual data analysis techniques to develop a model capable of detecting sarcasm in Kannada text. In current ages, the major study in research has been conducted in opinion mining, namely textual data existing on social media. Sarcasm is a witty, ironic, or satirical statement that can be delivered orally or in writing. Sarcasm is only a hair's breadth away from irony and satire. Because of its inherent ambiguity, humans find it difficult to recognise sarcasm. Because sarcasm is difficult to detect using simple sentiment analysis methods, its presence becomes certain. Sarcasm is detected using a variety of rule-based algorithms, statistical approaches, and machine learning classifiers which are to detect sarcasm in typescript written in English. There is less research on sarcasm detection in Indian languages such as Kannada. In this paper, various methods for detecting sarcasm are explored and analysed with a focus on the textual form of sarcasm in Kannada language.