A DESIGN OF SARCASM SENTIMENT DETECTION AND CLASSIFICATION MODEL
A DESIGN OF SARCASM SENTIMENT DETECTION ANDCLASSIFICATION MODEL USINGDEEP LEARNING TECHNIQUES
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evaluating the sentiment of all texts provides organizations with an overview of how positive and negative users are on a given issue. To use sarcasm is to be in a condition of discourse in which the author describes something obviously hostile to the listener or another person with the intent to insult or ridicule them. It is challenging to create a model that can accurately identify sarcasm in the field of natural language processing since sarcasm identification relies heavily on the context of utterances or phrases (NLP). Recent developments in deep learning (DL) models have an impact on ne...
evaluating the sentiment of all texts provides organizations with an overview of how positive and negative users are on a given issue. To use sarcasm is to be in a condition of discourse in which the author describes something obviously hostile to the listener or another person with the intent to insult or ridicule them. It is challenging to create a model that can accurately identify sarcasm in the field of natural language processing since sarcasm identification relies heavily on the context of utterances or phrases (NLP). Recent developments in deep learning (DL) models have an impact on neural networks (NN) in learning both lexical and contextual information, doing away with the need for manually constructed features in sarcasm detection. An automated sarcasm identification model has been developed to recognise the original emotion of a given text when sarcasm is present, allowing for accurate sarcasm detection.
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