Summary of the paper

Title SenTube: A Corpus for Sentiment Analysis on YouTube Social Media
Authors Olga Uryupina, Barbara Plank, Aliaksei Severyn, Agata Rotondi and Alessandro Moschitti
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.
Topics Opinion Mining / Sentiment Analysis, Social Media Processing
Full paper SenTube: A Corpus for Sentiment Analysis on YouTube Social Media
Bibtex @InProceedings{URYUPINA14.180,
  author = {Olga Uryupina and Barbara Plank and Aliaksei Severyn and Agata Rotondi and Alessandro Moschitti},
  title = {SenTube: A Corpus for Sentiment Analysis on YouTube Social Media},
  booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)},
  year = {2014},
  month = {may},
  date = {26-31},
  address = {Reykjavik, Iceland},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-8-4},
  language = {english}
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