Riccardo Cantini

Riccardo Cantini

PhD student in Information and Communication Technologies

DIMES, University of Calabria

Biography

Riccardo Cantini is a PhD student in Information and Communication Technologies at the Department of Computer Science, Modeling, Electronics and Systems Engineering of the University of Calabria, since 2019. Currently he is teaching assistant for Distributed Systems and Cloud/Edge Computing for the Internet of Things, master’s degree course in Computer Engineering for the IoT. He is also a member of the Scalable Computing and Cloud Laboratory (SCALab) at the University of Calabria since 2018. He was co-supervisor of several Computer Engineering theses, mainly in the field of social media and big data analysis, machine learning and deep learning.

His research interests include social media and big data analysis, deep learning, natural language processing, sentiment analysis, edge/fog computing, parallel and distributed data analysis.

Interests

  • Deep Learning
  • Natural Language Processing
  • Social Media and Big Data Analysis
  • Parallel and Distributed Data Analysis

Education

  • M.Sc. in Computer Engineering, 2019

    University of Calabria

  • B.Sc. in Computer Engineering, 2016

    University of Calabria

Recent Posts

Personality detection using BERT

In this post I show how to leverage BERT, a transformer-based language representation model, in order to identify the personality type of users based on their writing style and the content of their posts, according to the Myers-Briggs indicator (MBTI).
Personality detection using BERT

Play with BERT! Text classification using Huggingface and Tensorflow

In what follows, I’ll show how to fine-tune a BERT classifier, using Huggingface and Keras+Tensorflow, for dealing with two different text classification problems. The first consists in detecting the sentiment (negative or positive) of a movie review, while the second is related to the classification of a comment based on different types of toxicity, such as toxic, severe toxic, obscene, threat, insult and identity hate.
Play with BERT! Text classification using Huggingface and Tensorflow

Emotion detection from speech using Bi-directional LSTM networks and attention mechanism in Keras

This post is dedicated to the development of an artificial intelligence application capable of identifying the emotions expressed through the voice in spoken language. The classification model focuses on seven different emotions (anger, boredom, disgust, fear, happiness, sadness, neutral) and is enhanced with the attention mechanism.
Emotion detection from speech using Bi-directional LSTM networks and attention mechanism in Keras

Recent Publications

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Learning sentence-to-hashtags semantic mapping for hashtag recommendation on microblogs

The growing use of microblogging platforms is generating a huge amount of posts that need effective methods to be classified and …
Learning sentence-to-hashtags semantic mapping for hashtag recommendation on microblogs

Exploiting Machine Learning For Improving In-Memory Execution of Data-Intensive Workflows on Parallel Machines

Workflows are largely used to orchestrate complex sets of operations required to handle and process huge amounts of data. Parallel …
Exploiting Machine Learning For Improving In-Memory Execution of Data-Intensive Workflows on Parallel Machines

Learning Political Polarization on Social Media Using Neural Networks

Social media analysis is a fast growing research area aimed at extracting useful information from social media platforms. This paper …
Learning Political Polarization on Social Media Using Neural Networks

Contact

  • riccardo.cantini@unical.it
  • Cubo 41C, 5th floor, via P. Bucci, Rende (CS), Calabria 87036
  • Office hours: send an email for booking an online meeting on Microsoft Teams