Is sarcasm detection capability beneficial for sentiment analysis?

• Main Author: Koh, Brian Jin Kiong
• Other Authors: Wang Wenya
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Computer and Information Science
• Online Access: https://hdl.handle.net/10356/181202
• Institution: Nanyang Technological University

This project’s main goal is to weigh the pros and cons of integrating sarcasm detection into sentiment analysis within Natural Language Processing (NLP). Sentiment Analysis aims to find out the emotional tone behind text. However, sarcasm, which often tells a meaning opposite to the meaning of the literal words, makes this task harder. This study investigates whether the integration of sarcasm detection can help improve the accuracy and robustness of sentiment analysis models. By using advanced machine learning algorithms and linguistic techniques, this research assesses the potential for sarcasm detection to lower errors in sentiment classification, therefore improving the overall effectiveness of NLP models in interpreting human emotions in text.