Summary of the paper

Title Building a Sentiment Corpus of Tweets in Brazilian Portuguese
Authors Henrico Brum and Maria das Graças Volpe Nunes
Abstract The large amount of data available in social media, forums and websites motivates researches in several areas of Natural Language Processing, such as sentiment analysis. The popularity of the area due to its subjective and semantic characteristics motivates research on novel methods and approaches for classification. Hence, there is a high demand for datasets on different domains and different languages. This paper introduces TweetSentBR, a sentiment corpus for Brazilian Portuguese manually annotated with 15.000 sentences on TV show domain. The sentences were labeled in three classes (positive, neutral and negative) by seven annotators, following literature guidelines for ensuring reliability on the annotation. We also ran baseline experiments on polarity classification using six machine learning classifiers, reaching 80.38% on F-Measure in binary classification and 64.87% when including the neutral class. We also performed experiments in similar datasets for polarity classification task in comparison to this corpus.
Topics Social Media Processing, Opinion Mining / Sentiment Analysis, Corpus (Creation, Annotation, Etc.)
Full paper Building a Sentiment Corpus of Tweets in Brazilian Portuguese
Bibtex @InProceedings{BRUM18.389,
  author = {Henrico Brum and Maria das Graças Volpe Nunes},
  title = "{Building a Sentiment Corpus of Tweets in Brazilian Portuguese}",
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  month = {May 7-12, 2018},
  address = {Miyazaki, Japan},
  editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {979-10-95546-00-9},
  language = {english}
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