Analysis and Comparison of Deep Learning Networks for Supporting Sentiment Mining in Text Corpora

Giosue' Lo Bosco; Alfredo Cuzzocrea; Giovanni Pilato; Daniele Schicchi

Analysis and Comparison of Deep Learning Networks for Supporting Sentiment Mining in Text Corpora

Giosue' Lo Bosco, Alfredo Cuzzocrea, Giovanni Pilato, Daniele Schicchi

Risultato della ricerca: Conference contribution

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.

Lingua originale	English
Titolo della pubblicazione ospite	The 22nd International Conference on Information Integration and Web-based Applications & Services
Pagine	91-96
Numero di pagine	6
Stato di pubblicazione	Published - 2020

All Science Journal Classification (ASJC) codes

Human-Computer Interaction
Computer Networks and Communications
Computer Vision and Pattern Recognition
Software

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title = "Analysis and Comparison of Deep Learning Networks for Supporting Sentiment Mining in Text Corpora",

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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AU - Lo Bosco, Giosue'

AU - Cuzzocrea, Alfredo

AU - Pilato, Giovanni

AU - Schicchi, Daniele

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10447/481651

M3 - Conference contribution

SN - 978-145038922-8

SP - 91

EP - 96

BT - The 22nd International Conference on Information Integration and Web-based Applications & Services

ER -