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SiTAKA at SemEval-2017 Task 4: Sentiment Analysis in Twitter Based on a Rich Set of Features

  • Datos identificativos

    Identificador: imarina:9386104
    Handle: https://hdl.handle.net/20.500.11797/imarina9386104
    Autores:
    Jabreel MMoreno A
    Resumen:
    This paper describes SiTAKA, our system that has been used in task 4A, English and Arabic languages, Sentiment Analysis in Twitter of SemEval2017. The system proposes the representation of tweets using a novel set of features, which include a bag of negated words and the information provided by some lexicons. The polarity of tweets is determined by a classifier based on a Support Vector Machine. Our system ranks 2nd among 8 systems in the Arabic language tweets and ranks 8th among 38 systems in the English-language tweets.

  • Otros:

    Autor según el artículo: Jabreel M; Moreno A
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Moreno Ribas, Antonio
    Resumen: This paper describes SiTAKA, our system that has been used in task 4A, English and Arabic languages, Sentiment Analysis in Twitter of SemEval2017. The system proposes the representation of tweets using a novel set of features, which include a bag of negated words and the information provided by some lexicons. The polarity of tweets is determined by a classifier based on a Support Vector Machine. Our system ranks 2nd among 8 systems in the Arabic language tweets and ranks 8th among 38 systems in the English-language tweets.
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: antonio.moreno@urv.cat
    Identificador del autor: 0000-0003-3945-2314
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Referencia al articulo segun fuente origial: Proceedings Of The Annual Meeting Of The Association For Computational Linguistics. 694-699
    Referencia de l'ítem segons les normes APA: Jabreel M; Moreno A (2017). SiTAKA at SemEval-2017 Task 4: Sentiment Analysis in Twitter Based on a Rich Set of Features.
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2017
    Tipo de publicación: Proceedings Paper