Sarcasm Detection and Classification Using Deep Learning Model

The Twitter and Facebook kind of web platforms have gained significant traction as prominent mediums for individuals to document and articulate their emotions, viewpoints, and evaluations. The use of appropriate extraction methodologies, such as sentiment analysis, renders this data valuable across several domains. Sarcasm detection is a methodology used to examine individuals’ cognitive and affective states, with the aim of classifying. There are several methods by which individuals might articulate their sentiments. These attitudes are occasionally attended with sarcasm, particularly when expressing strong emotions. Sarcasm has often understood as a kind of communication in which a good statement is made with an underlying negative purpose. The majority of existing research endeavors see these responsibilities as separate entities. Until far, the majority of methods for sentiment and sarcasm classification have been focused on treating them as separate and independent text categorization tasks. Nowadays, there has been notable growth in the field of study using deep learning methods, resulting in substantial improvements in the performance of standalone classifiers. One of the primary challenges encountered by these methodologies is their inability to accurately categorize sarcastic statements as negative. Given this consideration, we assert that possessing the ability to identify sarcasm will contribute to the enhancement of sentiment classification, and conversely, proficiency in sentiment classification will aid in the recognition of sarcasm. Our research has shown a positive correlation between these two activities. This study presents a system that employs a deep neural network and multi-task learning to effectively predict the connection between different tasks, with the goal of attracting the overall concert of sentiment analysis. The approach presented in this study demonstrates superior performance compared to the previous methods, with a notable margin of 3% improvement, as shown by its F1-score of 97%.