| Sentiment analysis: Adjectives and adverbs are better than adjectives alone. F Benamara, C Cesarano, A Picariello, DR Recupero, VS Subrahmanian ICWSM 7, 203-206, 2007 | 508 | 2007 |
| Semantic web machine reading with FRED A Gangemi, V Presutti, D Reforgiato Recupero, AG Nuzzolese, ... Semantic Web 8 (6), 873-893, 2017 | 228* | 2017 |
| AVA: Adjective-verb-adverb combinations for sentiment analysis VS Subrahmanian, D Reforgiato IEEE Intelligent Systems 23 (4), 43-50, 2008 | 197 | 2008 |
| Deep learning and time series-to-image encoding for financial forecasting S Barra, SM Carta, A Corriga, AS Podda, DR Recupero IEEE/CAA Journal of Automatica Sinica 7 (3), 683-692, 2020 | 173 | 2020 |
| Multi-DQN: An ensemble of Deep Q-learning agents for stock market forecasting S Carta, A Ferreira, AS Podda, DR Recupero, A Sanna Expert systems with applications 164, 113820, 2021 | 170 | 2021 |
| Annotated rdf O Udrea, DR Recupero, VS Subrahmanian ACM Transactions on Computational Logic (TOCL) 11 (2), 1-41, 2010 | 163* | 2010 |
| Frame-based detection of opinion holders and topics: a model and a tool A Gangemi, V Presutti, DR Recupero IEEE Computational Intelligence Magazine 9 (1), 20-30, 2014 | 147 | 2014 |
| A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning S Carta, A Corriga, A Ferreira, AS Podda, DR Recupero Applied Intelligence 51, 889-905, 2021 | 109 | 2021 |
| Sentilo: frame-based sentiment analysis DR Recupero, V Presutti, S Consoli, A Gangemi, AG Nuzzolese Cognitive Computation 7 (2), 211-225, 2015 | 106 | 2015 |
| Bridging learning analytics and cognitive computing for big data classification in micro-learning video collections D Dessì, G Fenu, M Marras, DR Recupero Computers in Human Behavior 92, 468-477, 2019 | 104 | 2019 |
| Framester: A wide coverage linguistic linked data hub A Gangemi, M Alam, L Asprino, V Presutti, DR Recupero Knowledge Engineering and Knowledge Management: 20th International …, 2016 | 103 | 2016 |
| Fraud detection for E-commerce transactions by employing a prudential Multiple Consensus model S Carta, G Fenu, DR Recupero, R Saia Journal of Information Security and Applications 46, 13-22, 2019 | 91 | 2019 |
| A new unsupervised method for document clustering by using WordNet lexical and conceptual relations D Reforgiato Recupero Information Retrieval 10, 563-579, 2007 | 91 | 2007 |
| System and method for analysis of an opinion expressed in documents with regard to a particular topic VS Subrahmanian, DR Reforgiato, A Picariello, BJ Dorr, C Cesarano, ... US Patent 8,296,168, 2012 | 87 | 2012 |
| Antipole tree indexing to support range search and k-nearest neighbor search in metric spaces D Cantone, A Ferro, A Pulvirenti, DR Recupero, D Shasha IEEE transactions on knowledge and data engineering 17 (4), 535-550, 2005 | 84 | 2005 |
| Ensembling classical machine learning and deep learning approaches for morbidity identification from clinical notes V Kumar, DR Recupero, D Riboni, R Helaoui IEEE Access 9, 7107-7126, 2020 | 74 | 2020 |
| Generating knowledge graphs by employing natural language processing and machine learning techniques within the scholarly domain D Dessì, F Osborne, DR Recupero, D Buscaldi, E Motta Future Generation Computer Systems 116, 253-264, 2021 | 71 | 2021 |
| AI-KG: an automatically generated knowledge graph of artificial intelligence D Dessì, F Osborne, D Reforgiato Recupero, D Buscaldi, E Motta, H Sack The Semantic Web–ISWC 2020: 19th International Semantic Web Conference …, 2020 | 71 | 2020 |
| Oasys: An opinion analysis system C Cesarano, B Dorr, A Picariello, D Reforgiato, A Sagoff, ... AAAI Spring symposium on computational approaches to analyzing weblogs (CAAW …, 2006 | 69 | 2006 |
| Leveraging semantics for sentiment polarity detection in social media A Dridi, D Reforgiato Recupero International Journal of Machine Learning and Cybernetics 10, 2045-2055, 2019 | 66 | 2019 |
Aldo GangemiUniversity of Bologna and ISTC, National Research Council, ItalyVerified email at unibo.it
