Sarcasm Detection in Telugu Language Text Using Distinct Machine Learning Classification Algorithms

Sarcasm detection is a growing field in Natural Language Processing (NLP).Sarcasm is identified using positive or more increased positive words, often with a negative connotation, to insult or mock others.In sentiment analysis, detecting sarcasm in the text has become critical.They reviewed numerous relevant research articles, but due to the telugu language's limited resources, detecting sarcasm in telugu language texts remains challenging.As a result, the sentiment detection model struggles to accurately identify the exact sentiment of a sarcastic statement, necessitating the development of an automated sarcasm detection system.Many researchers have trained and tested various machine learning classification algorithms to identify sarcasm, but these algorithms require a dataset as input, which often contains noise.

Authors

Publication Details

Journal
International Journal of Computer Applications
Published
2024-09-30
DOI
https://doi.org/10.5120/ijca2024924040
Citations
1
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
0.35
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article

Sarcasm Detection in Telugu Language Text Using Distinct Machine Learning Classification Algorithms

B. RaviKiran, Srinivasu Badugu
1 citations
International Journal of Computer Applications
Hate Speech and Cyberbullying Detection
0.35
article

Sarcasm Detection in Telugu Language Text Using Distinct Machine Learning Classification Algorithms

B. RaviKiran, Srinivasu Badugu
article en
1 citations

Abstract

Sarcasm detection is a growing field in Natural Language Processing (NLP).Sarcasm is identified using positive or more increased positive words, often with a negative connotation, to insult or mock others.In sentiment analysis, detecting sarcasm in the text has become critical.They reviewed numerous relevant research articles, but due to the telugu language's limited resources, detecting sarcasm in telugu language texts remains challenging.As a result, the sentiment detection model struggles to accurately identify the exact sentiment of a sarcastic statement, necessitating the development of an automated sarcasm detection system.Many researchers have trained and tested various machine learning classification algorithms to identify sarcasm, but these algorithms require a dataset as input, which often contains noise.

International Journal of Computer ApplicationsVol. 186(42)
Quality Education
Openalex Percentile: Top 33%
Hate Speech and Cyberbullying Detection
0.35
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