What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Programming language
- The Internet
Her primary scientific interests are in Artificial intelligence, Natural language processing, Sentiment analysis, Task and Social media.
Artificial intelligence and Reuse are two areas of study in which she engages in interdisciplinary work.
Her research integrates issues of Irony, Social Semantic Web and Set in her study of Sentiment analysis.
Her study explores the link between Set and topics such as Semantics that cross with problems in World Wide Web and Semantic Web.
Her study on SemEval is often connected to Polarity as part of broader study in Task.
Her Social media study integrates concerns from other disciplines, such as Emotion recognition, Pragmatics, Corpus linguistics and Lexicon.
Her most cited work include:
- SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (230 citations)
- Developing Corpora for Sentiment Analysis: The Case of Irony and Senti-TUT (133 citations)
- Figurative messages and affect in Twitter (87 citations)
What are the main themes of her work throughout her whole career to date?
Viviana Patti mainly focuses on Artificial intelligence, Social media, Natural language processing, World Wide Web and Sentiment analysis.
Her Artificial intelligence research integrates issues from Irony and Task.
The Task study combines topics in areas such as Affect and Set.
Her research in Social media intersects with topics in Computational linguistics, Context, Social psychology and Internet privacy.
Her research investigates the connection between Natural language processing and topics such as Annotation that intersect with issues in Scheme.
Her World Wide Web study incorporates themes from Ontology and Semantics.
She most often published in these fields:
- Artificial intelligence (33.16%)
- Social media (26.02%)
- Natural language processing (24.49%)
What were the highlights of her more recent work (between 2018-2021)?
- Artificial intelligence (33.16%)
- Social media (26.02%)
- Natural language processing (24.49%)
In recent papers she was focusing on the following fields of study:
Viviana Patti focuses on Artificial intelligence, Social media, Natural language processing, Task and Voice activity detection.
Her work on Computational model, Irony detection and Leverage as part of general Artificial intelligence study is frequently linked to Matching and Polarization, therefore connecting diverse disciplines of science.
She integrates many fields, such as Social media and Immigration, in her works.
Viviana Patti studies Lexicon which is a part of Natural language processing.
Her Task study also includes
- Deep learning that intertwine with fields like Linguistic analysis and Recall,
- Benchmark and related Domain.
Her study in Social psychology is interdisciplinary in nature, drawing from both Sentiment analysis and Stance detection.
Between 2018 and 2021, her most popular works were:
- SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (230 citations)
- Cross-domain and Cross-lingual Abusive Language Detection: A Hybrid Approach with Deep Learning and a Multilingual Lexicon (29 citations)
- Time of your hate: The challenge of time in hate speech detection on social media (15 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- The Internet
Viviana Patti mostly deals with Task, Social media, Artificial intelligence, Natural language processing and Voice activity detection.
Task is frequently linked to Cognitive psychology in her study.
The various areas that Viviana Patti examines in her Social media study include Misinformation, Fake news, Media studies and Focus.
The concepts of her Focus study are interwoven with issues in Catalan, Software portability, Style and Set.
When carried out as part of a general Artificial intelligence research project, her work on Benchmark and SemEval is frequently linked to work in Immigration, therefore connecting diverse disciplines of study.
Her biological study spans a wide range of topics, including Deep learning and Lexicon.
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