Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques
One of the biggest problems with sentiment analysis systems is sarcasm. The use of implicit, indirect language to express opinions is what gives it its complexity. Sarcasm can be represented in a number of ways, such as in headings, conversations, or book titles. Even for a human, recognizing sarcas...
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| Format: | Article |
| Language: | English |
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Ediciones Universidad de Salamanca
2024-06-01
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| Series: | Advances in Distributed Computing and Artificial Intelligence Journal |
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| Online Access: | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31601 |
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| author | Neha Singh Umesh Chandra Jaiswal |
| author_facet | Neha Singh Umesh Chandra Jaiswal |
| author_sort | Neha Singh |
| collection | DOAJ |
| description | One of the biggest problems with sentiment analysis systems is sarcasm. The use of implicit, indirect language to express opinions is what gives it its complexity. Sarcasm can be represented in a number of ways, such as in headings, conversations, or book titles. Even for a human, recognizing sarcasm can be difficult because it conveys feelings that are diametrically contrary to the literal meaning expressed in the text. There are several different models for sarcasm detection. To identify humorous news headlines, this article assessed vectorization algorithms and several machine learning models. The recommended hybrid technique using the bag-of-words and TF-IDF feature vectorization models is compared experimentally to other machine learning approaches. In comparison to existing strategies, experiments demonstrate that the proposed hybrid technique with the bag-of-word vectorization model offers greater accuracy and F1-score results. |
| format | Article |
| id | doaj-art-de01426e1bb3458fae41ccdd96c0eba0 |
| institution | Kabale University |
| issn | 2255-2863 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Ediciones Universidad de Salamanca |
| record_format | Article |
| series | Advances in Distributed Computing and Artificial Intelligence Journal |
| spelling | doaj-art-de01426e1bb3458fae41ccdd96c0eba02025-01-23T11:25:18ZengEdiciones Universidad de SalamancaAdvances in Distributed Computing and Artificial Intelligence Journal2255-28632024-06-0113e31601e3160110.14201/adcaij.3160137082Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning TechniquesNeha Singh0Umesh Chandra Jaiswal1Department of ITCA, Madan Mohan Malaviya University of Technology, Gorakhpur, IndiaDepartment of ITCA, Madan Mohan Malaviya University of Technology, Gorakhpur, IndiaOne of the biggest problems with sentiment analysis systems is sarcasm. The use of implicit, indirect language to express opinions is what gives it its complexity. Sarcasm can be represented in a number of ways, such as in headings, conversations, or book titles. Even for a human, recognizing sarcasm can be difficult because it conveys feelings that are diametrically contrary to the literal meaning expressed in the text. There are several different models for sarcasm detection. To identify humorous news headlines, this article assessed vectorization algorithms and several machine learning models. The recommended hybrid technique using the bag-of-words and TF-IDF feature vectorization models is compared experimentally to other machine learning approaches. In comparison to existing strategies, experiments demonstrate that the proposed hybrid technique with the bag-of-word vectorization model offers greater accuracy and F1-score results.https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31601sentiment analysismachine learningnews headlinesvectorization model |
| spellingShingle | Neha Singh Umesh Chandra Jaiswal Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques Advances in Distributed Computing and Artificial Intelligence Journal sentiment analysis machine learning news headlines vectorization model |
| title | Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques |
| title_full | Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques |
| title_fullStr | Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques |
| title_full_unstemmed | Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques |
| title_short | Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques |
| title_sort | sarcasm text detection on news headlines using novel hybrid machine learning techniques |
| topic | sentiment analysis machine learning news headlines vectorization model |
| url | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31601 |
| work_keys_str_mv | AT nehasingh sarcasmtextdetectiononnewsheadlinesusingnovelhybridmachinelearningtechniques AT umeshchandrajaiswal sarcasmtextdetectiononnewsheadlinesusingnovelhybridmachinelearningtechniques |