Articles producció científicaEnginyeria Informàtica i Matemàtiques

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

  • Identification data

    Identifier:  imarina:9386104
    Handle: https://hdl.handle.net/20.500.11797/imarina9386104
    Authors:  Jabreel M; Moreno A
    Abstract:
    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.

  • Others:

    Link to the original source: https://aclanthology.org/S17-2115/
    APA: Jabreel M; Moreno A (2017). SiTAKA at SemEval-2017 Task 4: Sentiment Analysis in Twitter Based on a Rich Set of Features.
    Paper original source: Proceedings Of The Annual Meeting Of The Association For Computational Linguistics. 694-699
    Journal publication year: 2017
    Entity: Universitat Rovira i Virgili
    Paper version: info:eu-repo/semantics/publishedVersion
    Record's date: 2024-10-12
    URV's Author/s: Moreno Ribas, Antonio
    Department: Enginyeria Informàtica i Matemàtiques
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Publication Type: Proceedings Paper
    Author, as appears in the article.: Jabreel M; Moreno A
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: antonio.moreno@urv.cat