Sarcasm Recognition in Twitter - Thakur, Sakshi; Singh, Sarbjeet; Singh, Makhan (International Journal of Computer Sciences and Engineering, 2019)
Sarcasm is a nuanced form of speech broadly utilized in different online platforms such as social networks, microblogs and sarcasm recognition refers to anticipate whether the content is sarcastic or not. Identifying sarcasm in content is among the significant issues confronting sentiment analysis. In sarcasm, individuals express their negative feelings by utilizing positive or strengthened positive words in the content. While talking, individuals regularly utilize intense tonal force and certain gestural pieces of information like rolling of the eyes, hand development, and so forth to reveal sarcasm. Due to these challenges, in the last few decades, researchers have been working rigorously on sarcasm recognition so as to amend the performance of automatic sentiment analysis of data. In this paper, a supervised learning approach, which learns from four different categories of features and their combinations, is presented. These feature sets are employed to classify instances as sarcastic and not-sarcastic using four different classifiers, namely – Naïve Bayes, SVMs, Random Forest and k-Nearest Neighbor classifiers. In particular, it has been tried to explore the impact of sarcastic patterns based on POS tags and the outcomes demonstrate that they are not useful as a feature set for recognizing sarcasm when compared to content words and function words. Using the finest feature set i.e. the combination of content words and function words, a precision and AUC of approximately 85% and 87%, respectively, were achieved. Additionally, the Naïve Bayes classifier gives better results over every single other classifier that has been utilized.