Summary of the paper

Title Resource Creation and Evaluation for Multilingual Sentiment Analysis in Social Media Texts
Authors Alexandra Balahur, Marco Turchi, Ralf Steinberger, Jose Manuel Perea-Ortega, Guillaume Jacquet, Dilek Kucuk, Vanni Zavarella and Adil El Ghali
Abstract This paper presents an evaluation of the use of machine translation to obtain and employ data for training multilingual sentiment classifiers. We show that the use of machine translated data obtained similar results as the use of native-speaker translations of the same data. Additionally, our evaluations pinpoint to the fact that the use of multilingual data, including that obtained through machine translation, leads to improved results in sentiment classification. Finally, we show that the performance of the sentiment classifiers built on machine translated data can be improved using original data from the target language and that even a small amount of such texts can lead to significant growth in the classification performance.
Topics Multilinguality, Document Classification, Text categorisation
Full paper Resource Creation and Evaluation for Multilingual Sentiment Analysis in Social Media Texts
Bibtex @InProceedings{BALAHUR14.965,
  author = {Alexandra Balahur and Marco Turchi and Ralf Steinberger and Jose Manuel Perea-Ortega and Guillaume Jacquet and Dilek Kucuk and Vanni Zavarella and Adil El Ghali},
  title = {Resource Creation and Evaluation for Multilingual Sentiment Analysis in Social Media Texts},
  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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