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Title: Harnessing Online News for Sarcasm Detection in Hindi Tweets
Authors: Bharti, Santosh Kumar
Korra, Sathya Babu
Jena, Sanjay Kumar
Keywords: Hindi tweets
Online news
Issue Date: Dec-2017
Citation: 7th International Conference on Pattern Recognition and Machine Intelligence, Kolkata, India, 5 – 8 December, 2017
Abstract: Detection of sarcasm in Indian languages is one of the most challenging tasks of Natural Language Processing (NLP) because Indian languages are ambiguous in nature and rich in morphology. Though Hindi is the fourth popular language in the world, sarcasm detection in it remains unexplored. One of the reasons is the lack of annotated resources. In the absence of sufficient resources, processing the NLP tasks such as POS tagging, sentiment analysis, text mining, sarcasm detection, etc., becomes tough for researchers. Here, we proposed a framework for sarcasm detection in Hindi tweets using online news. In this article, the online news is considered as the context of a given tweet during the detection of sarcasm. The proposed framework attains an accuracy of 79.4%.
Description: Copyright of this document belongs to proceedings publisher.
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