What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Programming language
His main research concerns Artificial intelligence, Information retrieval, Natural language processing, Sentiment analysis and Term.
Fabrizio Sebastiani combines subjects such as Machine learning and Pattern recognition with his study of Artificial intelligence.
Fabrizio Sebastiani interconnects Software portability and Categorization in the investigation of issues within Machine learning.
His Categorization research includes themes of Multi-task learning, Document classification and Knowledge engineering.
His research in Information retrieval intersects with topics in Static web page and Web page.
The concepts of his Sentiment analysis study are interwoven with issues in WordNet and Data science.
His most cited work include:
- Machine learning in automated text categorization (6659 citations)
- SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining (2060 citations)
- SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. (2012 citations)
What are the main themes of his work throughout his whole career to date?
Fabrizio Sebastiani focuses on Artificial intelligence, Information retrieval, Machine learning, Natural language processing and Supervised learning.
His work is dedicated to discovering how Artificial intelligence, Pattern recognition are connected with Contextual image classification and other disciplines.
His work on Support vector machine as part of general Machine learning study is frequently connected to Weighting, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Fabrizio Sebastiani focuses mostly in the field of Natural language processing, narrowing it down to topics relating to Categorization and, in certain cases, Lexicon.
His Supervised learning research incorporates elements of Class, Information extraction and Set.
His Sentiment analysis study integrates concerns from other disciplines, such as Computational linguistics, WordNet, Product, Set and Data science.
He most often published in these fields:
- Artificial intelligence (52.38%)
- Information retrieval (28.14%)
- Machine learning (22.94%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (52.38%)
- Machine learning (22.94%)
- Supervised learning (15.15%)
In recent papers he was focusing on the following fields of study:
Fabrizio Sebastiani mainly focuses on Artificial intelligence, Machine learning, Supervised learning, Natural language processing and Set.
His work on Class, Sentiment analysis and Classifier as part of general Artificial intelligence study is frequently linked to Frequency, therefore connecting diverse disciplines of science.
His research in the fields of Statistical classification overlaps with other disciplines such as Current.
As a part of the same scientific study, he usually deals with the Supervised learning, concentrating on Structured prediction and frequently concerns with Product.
The various areas that Fabrizio Sebastiani examines in his Natural language processing study include Domain, Ensemble learning, Feature vector and Transfer of learning.
His studies in Set integrate themes in fields like Medieval Latin and Classifier.
Between 2013 and 2021, his most popular works were:
- SemEval-2016 Task 4: Sentiment Analysis in Twitter (283 citations)
- Hierarchical Multi-label Conditional Random Fields for Aspect-Oriented Opinion Mining (44 citations)
- Tweet Sentiment: From Classification to Quantification (43 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
Fabrizio Sebastiani mostly deals with Artificial intelligence, Machine learning, Sentiment analysis, Supervised learning and Frequency.
His Artificial intelligence research incorporates themes from Domain, Meaning and Natural language processing.
His Natural language processing study typically links adjacent topics like Robustness.
His studies deal with areas such as Classifier and Data mining as well as Machine learning.
His work in Data mining addresses subjects such as Training set, which are connected to disciplines such as Information retrieval.
Fabrizio Sebastiani has included themes like Kullback–Leibler divergence, Binary number, Structured prediction, Multivariate statistics and Heuristics in his Supervised learning study.
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