Sarcasm Detection in Hindi Tweets: A Review

Abstract

Sentiment analysis is a method to identify people’s opinion, attitude, sentiment, and emotion in the direction of any precise goal which include individuals, events, topics, products, organizations, services, etc. Sarcasm is a special type of sentiment that comprise of words which might be opposite in that means to what is in reality being said (specially in a sense of insult, wit, irritation, humor). Sarcasm detection in Indian language Hindi is a challenging task in Natural Language Processing (NLP) because of the richness of morphology, Hindi being a fourth popular language within the world stay unexplored in sarcasm detection. Nowadays, posting Hindi sarcastic message on social media like Twitter, Facebook, WhatsApp, etc. has become a new trend to avoid direct negativity. Detecting those indirect negativities i.e., sarcasm within the social media Hindi textual content has come to be a vital undertaking as they influence each business organization. The belongings of sarcasm that makes it hard to analyze and detect is the gap among its literal and meant meaning. Therefore, an automated machine is needed for sarcasm detection in textual records which could be capable of identifying real sentiment of a given text in the presence of sarcasm. In the absence of sufficient resources, processing the NLP tasks which include POS tagging, sentiment analysis, text mining, sarcasm detection, etc., becomes hard for researchers. Here, we proposed a review for sarcasm detection in Hindi tweets.

Sarcasm detection methods inside the textual content may be labeled as rule-based, pattern-primarily based, system learning-based totally and context-primarily based.