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Ioannis Katakis
Researcher at National and Kapodistrian University of Athens
Publications - 51
Citations - 6155
Ioannis Katakis is an academic researcher from National and Kapodistrian University of Athens. The author has contributed to research in topics: Sentiment analysis & Voting. The author has an hindex of 17, co-authored 49 publications receiving 5465 citations. Previous affiliations of Ioannis Katakis include Aristotle University of Thessaloniki & University of Cyprus.
Papers
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Proceedings Article
Towards detection of faulty traffic sensors in real-time
Nikolas Zygouras,Nikolaos Panagiotou,Ioannis Katakis,Dimitrios Gunopulos,Nikos Zacheilas,Ioannis Boutsis,Vana Kalogeraki +6 more
TL;DR: An efficient solution to resolve in real-time the source of irregular readings by examining the correlation and the consistency among neighbor sensors and exploiting the wisdom of the crowd is proposed.
Mining Domain-Specific Dictionaries of Opinion Words
TL;DR: The authors proposed an approach for domain-specific dictionary building and evaluated it on a sentiment analysis task on user reviews on digital devices, and presented NiosTo, a software that enables dictionary extraction and sentiment analysis on a given corpus.
Journal ArticleDOI
Hierarchical partitioning of the output space in multi-label data
TL;DR: Hierarchy of Multi-label classifiers (HOMER) as discussed by the authors is a multi-label learning algorithm that breaks the initial learning task to several, easier sub-tasks by first constructing a hierarchy of labels from a given label set and then employing a given base MLC to the resulting sub-problems.
Journal ArticleDOI
Learning patterns for discovering domain-oriented opinion words
Pantelis Agathangelou,Ioannis Katakis,Ioannis Koutoulakis,Fotis Kokkoras,Dimitrios Gunopulos +4 more
TL;DR: The approach (DidaxTo) utilizes opinion modifiers, sentiment consistency theories, polarity assignment graphs and pattern similarity metrics, and is compared against lexicons extracted by the state-of-the-art approaches on a sentiment analysis task.
Modern Applications of Machine Learning
TL;DR: This paper presents the above three application domains as well as some recent efforts, where machine learning techniques are applied in order to analyze the data provided by these domains.