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

  • Dades identificatives

    Identificador: imarina:9386104
    Handle: https://hdl.handle.net/20.500.11797/imarina9386104
    Autors:
    Jabreel MMoreno A
    Resum:
    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.

  • Altres:

    Autor segons l'article: Jabreel M; Moreno A
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Moreno Ribas, Antonio
    Resum: 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.
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: antonio.moreno@urv.cat
    Identificador de l'autor: 0000-0003-3945-2314
    Data d'alta del registre: 2024-10-12
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Referència a l'article segons font original: Proceedings Of The Annual Meeting Of The Association For Computational Linguistics. 694-699
    Referència 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 Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2017
    Tipus de publicació: Proceedings Paper