Comparing Deep-Learning Architectures and Traditional Machine-Learning Approaches for Satire Identification in Spanish Tweets
| Autor: | José Antonio García-Díaz, Rafael Valencia-García, Oscar Apolinario-Arzube, Harry Luna-Aveiga, José Medina-Moreira |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
text classification
Computer science General Mathematics 02 engineering and technology Machine learning computer.software_genre 020204 information systems Classifier (linguistics) 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) natural language processing Engineering (miscellaneous) Artificial neural network business.industry Deep learning lcsh:Mathematics Sentiment analysis automatic satire identification lcsh:QA1-939 language.human_language Identification (information) Mexican Spanish language 020201 artificial intelligence & image processing Artificial intelligence User interface business computer Meaning (linguistics) |
| Zdroj: | Mathematics, Vol 8, Iss 2075, p 2075 (2020) Mathematics Volume 8 Issue 11 |
| ISSN: | 2227-7390 |
| Popis: | Automatic satire identification can help to identify texts in which the intended meaning differs from the literal meaning, improving tasks such as sentiment analysis, fake news detection or natural-language user interfaces. Typically, satire identification is performed by training a supervised classifier for finding linguistic clues that can determine whether a text is satirical or not. For this, the state-of-the-art relies on neural networks fed with word embeddings that are capable of learning interesting characteristics regarding the way humans communicate. However, as far as our knowledge goes, there are no comprehensive studies that evaluate these techniques in Spanish in the satire identification domain. Consequently, in this work we evaluate several deep-learning architectures with Spanish pre-trained word-embeddings and compare the results with strong baselines based on term-counting features. This evaluation is performed with two datasets that contain satirical and non-satirical tweets written in two Spanish variants: European Spanish and Mexican Spanish. Our experimentation revealed that term-counting features achieved similar results to deep-learning approaches based on word-embeddings, both outperforming previous results based on linguistic features. Our results suggest that term-counting features and traditional machine learning models provide competitive results regarding automatic satire identification, slightly outperforming state-of-the-art models. |
| Databáze: | OpenAIRE |
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