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...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad de Salamanca (USAL) |
| Repositorio: | GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| OAI Identifier: | oai:gredos.usal.es:10366/162527 |
| Acceso en línea: | http://hdl.handle.net/10366/162527 |
| Access Level: | acceso abierto |
| Palabra clave: | Sentiment analysis Machine learning News headlines Vectorization model |
| Sumario: | 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. |
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