ILab-Edinburgh at SemEval-2016 task 7: A hybrid approach for determining sentiment intensity of Arabic Twitter phrases

Eshrag Refaee, Verena Rieser

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

This paper describes the iLab-Edinburgh Sentiment Analysis system, winner of the Arabic Twitter Task 7 in SemEval-2016. The system employs a hybrid approach of supervised learning and rule-based methods to predict a sentiment intensity (SI) score for a given Arabic Twitter phrase. First, the supervised method uses an ensemble of trained linear regression models to produce an initial SI score for each given text instance. Second, the resulting SI score is adjusted using a set of rules that exploit a number of publicly available sentiment lexica. The system demonstrates strong results of 0.536 Kendall score, ranking top in this task.

Original languageEnglish
Title of host publicationProceedings of SemEval 2016
PublisherAssociation for Computational Linguistics
Pages474-480
Number of pages7
ISBN (Electronic)9781941643952
Publication statusPublished - 2016
Event10th International Workshop on Semantic Evaluation 2016 - San Diego, California, United States
Duration: 16 Jun 201617 Jun 2016

Conference

Conference10th International Workshop on Semantic Evaluation 2016
Abbreviated titleSemEval 2016
CountryUnited States
CitySan Diego, California
Period16/06/1617/06/16

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Computer Science Applications

Fingerprint Dive into the research topics of 'ILab-Edinburgh at SemEval-2016 task 7: A hybrid approach for determining sentiment intensity of Arabic Twitter phrases'. Together they form a unique fingerprint.

  • Cite this

    Refaee, E., & Rieser, V. (2016). ILab-Edinburgh at SemEval-2016 task 7: A hybrid approach for determining sentiment intensity of Arabic Twitter phrases. In Proceedings of SemEval 2016 (pp. 474-480). Association for Computational Linguistics.