SenTube: A corpus for sentiment analysis on YouTube social media

Olga Uryupina, Barbara Plank, Aliaksei Severyn, Agata Rotondi, Alessandro Moschitti

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

16 Citations (Scopus)

Abstract

In this paper we present SenTube - a dataset of user-generated comments on YouTube videos annotated for information content and sentiment polarity. It contains annotations that allow to develop classifiers for several important NLP tasks: (i) sentiment analysis, (ii) text categorization (relatedness of a comment to video and/or product), (iii) spam detection, and (iv) prediction of comment informativeness. The SenTube corpus favors the development of research on indexing and searching YouTube videos exploiting information derived from comments. The corpus will cover several languages: at the moment, we focus on English and Italian, with Spanish and Dutch parts scheduled for the later stages of the project. For all the languages, we collect videos for the same set of products, thus offering possibilities for multi- and cross-lingual experiments. The paper provides annotation guidelines, corpus statistics and annotator agreement details.

Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014
PublisherEuropean Language Resources Association (ELRA)
Pages4244-4249
Number of pages6
ISBN (Electronic)9782951740884
Publication statusPublished - 1 Jan 2014
Event9th International Conference on Language Resources and Evaluation, LREC 2014 - Reykjavik, Iceland
Duration: 26 May 201431 May 2014

Other

Other9th International Conference on Language Resources and Evaluation, LREC 2014
CountryIceland
CityReykjavik
Period26/5/1431/5/14

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Keywords

  • Annotation
  • Sentiment analysis
  • Social media

ASJC Scopus subject areas

  • Linguistics and Language
  • Library and Information Sciences
  • Education
  • Language and Linguistics

Cite this

Uryupina, O., Plank, B., Severyn, A., Rotondi, A., & Moschitti, A. (2014). SenTube: A corpus for sentiment analysis on YouTube social media. In Proceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014 (pp. 4244-4249). European Language Resources Association (ELRA).