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      Sentiment analysis for reviews and microtexts based on lexico-syntactic knowledge

      Published
      proceedings-article
      Fifth BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2013) (FDIA)
      Future Directions in Information Access (FDIA 2013)
      3 September 2013
      Sentiment Analysis, Opinion Mining, Dependency Parsing, Machine Learning
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            Abstract

            We describe two methods to perform sentiment analysis both on long and short texts written in Spanish language. We first present an unsupervised method based on dependency parsing which calculates the semantic orientation (SO) of the sentences in order to classify the polarity. We then propose a hybrid approach which uses the computed SO and lexico-syntactic knowledge as features for a supervised classifier. Experimental results show the utility of employing syntactic information to classify the polarity in both types of texts and the importance of defining mechanisms to adapt the system for a specific domain and social medium.

            Content

            Author and article information

            Contributors
            Conference
            September 2013
            September 2013
            : 38-43
            Affiliations
            [0001]Departamento de Computación, Universidade da Coruña

            Spain
            Article
            10.14236/ewic/FDIA2013.8
            7899d173-d446-40f3-b611-3d7b335f1539
            © David Vilares. Published by BCS Learning and Development Ltd. Fifth BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2013), Granada, Spain

            This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

            Fifth BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2013)
            FDIA
            5
            Granada, Spain
            3 September 2013
            Electronic Workshops in Computing (eWiC)
            Future Directions in Information Access (FDIA 2013)
            History
            Product

            1477-9358 BCS Learning & Development

            Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/FDIA2013.8
            Self URI (journal page): https://ewic.bcs.org/
            Categories
            Electronic Workshops in Computing

            Applied computer science,Computer science,Security & Cryptology,Graphics & Multimedia design,General computer science,Human-computer-interaction
            Sentiment Analysis,Opinion Mining,Dependency Parsing,Machine Learning

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