Semantic Sentiment Analysis Challenge at ESWC2017
作者： Diego Reforgiato Recupero , Erik Cambria , Emanuele Di Rosa
DOI: 10.1007/978-3-319-69146-6_10
关键词:
摘要: Sentiment Analysis is a widely studied research field in both and industry, there are different approaches for addressing sentiment analysis related tasks. engines implement spanning from lexicon-based techniques, to machine learning, or involving syntactical rules analysis. Such systems already evaluated international challenges. However, Semantic approaches, which take into account rely also on large semantic knowledge bases Web best practices, not under specific experimental evaluation comparison by other may potentially deliver higher performance, since they able analyze the implicit, semantics features associated with natural language concepts. In this paper, we present fourth edition of Challenge, implementing relying competition test sets, Systems merely based syntax/word-count just have been excluded evaluation. Then, results each task show winner most innovative approach award, that combines several task.
springer.com
core.ac.uk
sci-hub.se 参考文章(16)
Diego Reforgiato Recupero, Erik Cambria, None, ESWC'14 Challenge on Concept-Level Sentiment Analysis Semantic Web Evaluation Challenges. pp. 211- 222 ,(2014) , 10.1007/978-3-319-25518-7_18
Diego Reforgiato Recupero, Valentina Presutti, Sergio Consoli, Aldo Gangemi, Andrea Giovanni Nuzzolese, Sentilo: Frame-Based Sentiment Analysis Cognitive Computation. ,vol. 7, pp. 211- 225 ,(2015) , 10.1007/S12559-014-9302-Z
Aldo Gangemi, Valentina Presutti, Diego Reforgiato Recupero, Frame-Based Detection of Opinion Holders and Topics: A Model and a Tool IEEE Computational Intelligence Magazine. ,vol. 9, pp. 20- 30 ,(2014) , 10.1109/MCI.2013.2291688
Mark Dredze, John Blitzer, Fernando Pereira, Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification meeting of the association for computational linguistics. pp. 440- 447 ,(2007)
Giulio Petrucci, Mauro Dragoni, An Information Retrieval-Based System for Multi-domain Sentiment Analysis Semantic Web Evaluation Challenges. pp. 234- 243 ,(2015) , 10.1007/978-3-319-25518-7_20
Semantic Web Evaluation Challenges Communications in computer and information science. ,vol. 548, pp. 165- 176 ,(2014) , 10.1007/978-3-319-25518-7
Marco Federici, Mauro Dragoni, A Knowledge-Based Approach for Aspect-Based Opinion Mining Semantic Web Evaluation Challenge. pp. 141- 152 ,(2016) , 10.1007/978-3-319-46565-4_11
Semantic Web Challenges Springer International Publishing. ,(2016) , 10.1007/978-3-319-46565-4
Andi Rexha, Mark Kröll, Mauro Dragoni, Roman Kern, Exploiting Propositions for Opinion Mining Semantic Sentiment Analysis Challenge. pp. 121- 125 ,(2016) , 10.1007/978-3-319-46565-4_9
Mauro Dragoni, Diego Reforgiato Recupero, Challenge on Fine-Grained Sentiment Analysis Within ESWC2016 Semantic Web Evaluation Challenge. pp. 79- 94 ,(2016) , 10.1007/978-3-319-46565-4_6