In this paper, we tackle the problem of the irony and sarcasm detection for the Italian language to contribute to the enrichment of the sentiment analysis field. We analyze and compare five deep-learning systems. Results show the high suitability of such systems to face the problem by achieving 93% of F1-Score in the best case. Furthermore, we briefly analyze the model architectures in order to choose the best compromise between performances and complexity.
Alcamo T., Cuzzocrea A., Lo Bosco G., Pilato G., Schicchi D. (2020). Analysis and Comparison of Deep Learning Networks for Supporting Sentiment Mining in Text Corpora. In The 22nd International Conference on Information Integration and Web-based Applications & Services (pp. 91-96). Association for Computing Machinery [10.1145/3428757.3429144].
Analysis and Comparison of Deep Learning Networks for Supporting Sentiment Mining in Text Corpora
Lo Bosco G.;Pilato G.;Schicchi D.
2020-01-01
Abstract
In this paper, we tackle the problem of the irony and sarcasm detection for the Italian language to contribute to the enrichment of the sentiment analysis field. We analyze and compare five deep-learning systems. Results show the high suitability of such systems to face the problem by achieving 93% of F1-Score in the best case. Furthermore, we briefly analyze the model architectures in order to choose the best compromise between performances and complexity.
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