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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 MMoreno 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:

    Author, as appears in the article.: Jabreel M; Moreno A
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Moreno Ribas, Antonio
    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.
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: antonio.moreno@urv.cat
    Author identifier: 0000-0003-3945-2314
    Record's date: 2024-10-12
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://aclanthology.org/S17-2115/
    Papper original source: Proceedings Of The Annual Meeting Of The Association For Computational Linguistics. 694-699
    APA: Jabreel M; Moreno A (2017). SiTAKA at SemEval-2017 Task 4: Sentiment Analysis in Twitter Based on a Rich Set of Features.
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2017
    Publication Type: Proceedings Paper