Novel Deep Neural Network Approach for the Sarcasm Detection in Hindi Language
Keywords:
Sentiment Analysis (SA), Natural Language Processing (NLP), BiLSTM, Neural Network (NN)Abstract
Sentiment analysis, also known as opinion mining, is a computational technique used to determine and classify sentiments expressed in text data. With the increasing popularity of social media platforms and the vast amount of user-generated content in Hindi, there is a growing need for effective sentiment analysis tools specifically designed for the Hindi language. As more individuals from diverse age groups and languages start using the internet, we need it in regional languages. Up until now, the majority of SA research has been done in English. However, very little study has been done on Indian languages, with the exception of a few. One of the Indian languages, Hindi, is the focus of this study on SA.
The work on sentiments like positive, negative, and neutral has been done previously here in this research one more very complex sentiment is detected which is sarcasm. Sarcasm is a complex linguistic phenomenon that involves expressing irony or mockery through words or phrases that convey the opposite meaning of the intended message. Accurate detection of sarcasm in textual data is a challenging task, especially in languages like Hindi, which possess rich contextual nuances and linguistic intricacies. The novel neural network along with their results are presented in the paper.
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