Sentiment Mining and Analysis over Text Corpora via Complex Deep
Learning Neural Architectures
(pp448-461)
Teresa Alcamo, Alfredo
Cuzzocrea, Giovanni Pilato, and Daniele Schicchi
doi:
https://doi.org/10.26421/JDI2.4-4
Abstracts:
We analyze and
compare five deep-learning neural architectures to manage the
problem of irony and sarcasm detection for the Italian language. We
briefly analyze the model architectures to choose the best
compromise between performances and complexity. The obtained results
show the effectiveness of such systems to handle the problem by
achieving 93\%
of
F1-Score
in the best case. As a case study, we also illustrate a possible
embedding of the neural systems in a cloud computing infrastructure
to exploit the computational advantage of using such an approach in
tackling big data.
Key words:
Sentiment Mining;
Sentiment Analysis; Text Corpora; Deep Learning; Neural
Architectures; Complex Architectures
