Summary of the paper

Title A Hungarian Sentiment Corpus Manually Annotated at Aspect Level
Authors Martina Katalin Szabó, Veronika Vincze, Katalin Ilona Simkó, Viktor Varga and Viktor Hangya
Abstract In this paper we present a Hungarian sentiment corpus manually annotated at aspect level. Our corpus consists of Hungarian opinion texts written about different types of products. The main aim of creating the corpus was to produce an appropriate database providing possibilities for developing text mining software tools. The corpus is a unique Hungarian database: to the best of our knowledge, no digitized Hungarian sentiment corpus that is annotated on the level of fragments and targets has been made so far. In addition, many language elements of the corpus, relevant from the point of view of sentiment analysis, got distinct types of tags in the annotation. In this paper, on the one hand, we present the method of annotation, and we discuss the difficulties concerning text annotation process. On the other hand, we provide some quantitative and qualitative data on the corpus. We conclude with a description of the applicability of the corpus.
Topics Corpus (Creation, Annotation, etc.), Opinion Mining / Sentiment Analysis, Social Media Processing
Full paper A Hungarian Sentiment Corpus Manually Annotated at Aspect Level
Bibtex @InProceedings{SZAB16.815,
  author = {Martina Katalin Szabó and Veronika Vincze and Katalin Ilona Simkó and Viktor Varga and Viktor Hangya},
  title = {A Hungarian Sentiment Corpus Manually Annotated at Aspect Level},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
  year = {2016},
  month = {may},
  date = {23-28},
  location = {Portoro┼ż, Slovenia},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {978-2-9517408-9-1},
  language = {english}
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