F
Fabrizio Sebastiani
Researcher at Istituto di Scienza e Tecnologie dell'Informazione
Publications - 235
Citations - 20767
Fabrizio Sebastiani is an academic researcher from Istituto di Scienza e Tecnologie dell'Informazione. The author has contributed to research in topics: Supervised learning & Computer science. The author has an hindex of 42, co-authored 220 publications receiving 19289 citations. Previous affiliations of Fabrizio Sebastiani include University of Glasgow & Khalifa University.
Papers
More filters
Journal ArticleDOI
Machine learning in automated text categorization
TL;DR: This survey discusses the main approaches to text categorization that fall within the machine learning paradigm and discusses in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.
Proceedings Article
SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining.
TL;DR: This work discusses SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and opinion mining applications, and reports on the improvements concerning aspect (b) that it embodies with respect to version 1.0.
Proceedings Article
SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining
Andrea Esuli,Fabrizio Sebastiani +1 more
TL;DR: SENTIWORDNET is a lexical resource in which each WORDNET synset is associated to three numerical scores Obj, Pos and Neg, describing how objective, positive, and negative the terms contained in the synset are.
Proceedings ArticleDOI
SemEval-2016 Task 4: Sentiment Analysis in Twitter
TL;DR: The SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions. as mentioned in this paper discusses the fourth year of the Sentiment Analysis in Twitter Task and discusses the three new subtasks focus on two variants of the basic sentiment classification in Twitter task.
Proceedings ArticleDOI
Supervised term weighting for automated text categorization
TL;DR: It is proposed that learning from training data should also affect phase (ii), i.e. that information on the membership of training documents to categories be used to determine term weights, and is called supervised term weighting (STW).