@InProceedings{bobicev-sokolova:2017:RANLP,
  author    = {Bobicev, Victoria  and  Sokolova, Marina},
  title     = {Inter-Annotator Agreement in Sentiment Analysis: Machine Learning Perspective},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {97--102},
  abstract  = {Manual text annotation is an essential part of Big Text analytics. Although
	annotators work with limited parts of data sets, their results are extrapolated
	by automated text classification and affect the final classification results.
	Reliability of annotations and adequacy of assigned labels are especially
	important in the case of sentiment annotations. In the current study we examine
	inter-annotator agreement in multi-class, multi-label sentiment annotation of
	messages. We used several annotation agreement measures, as well as statistical
	analysis and Machine Learning to assess the resulting annotations.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_015}
}