GTI at SemEval-2016 task 4: Training a naive bayes classifier using features of an unsupervised system

Autor:	Milagros Fernández-Gavilanes, Enrique Costa-Montenegro, Francisco J. González-Castaño, Tamara Álvarez-López, Jonathan Juncal-Martínez
Předmět:	Scale (ratio) business.industry Computer science InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL 05 social sciences Sentiment analysis 02 engineering and technology Lexicon Machine learning computer.software_genre SemEval 0506 political science Task (project management) Naive Bayes classifier Dependency grammar 050602 political science & public administration 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence InformationSystems_MISCELLANEOUS business computer Natural language processing
Zdroj:	ResearcherID SemEval@NAACL-HLT Scopus-Elsevier Milagros Fernández-Gavilanes
Popis:	This paper presents the approach of the GTI Research Group to SemEval-2016 task 4 on Sentiment Analysis in Twitter, or more specifically, subtasks A (Message Polarity Classification), B (Tweet classification according to a two-point scale) and D (Tweet quantification according to a two-point scale). We followed a supervised approach based on the extraction of features by a dependency parsing-based approach using a sentiment lexicon and Natural Language Processing techniques.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a132c0e210172c31c5dbd09266c9021 http://www.scopus.com/inward/record.url?eid=2-s2.0-85035795626&partnerID=MN8TOARS Zobrazit plný text záznamu