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.- Anthology ID:
- R17-1015
- Volume:
- Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
- Month:
- September
- Year:
- 2017
- Address:
- Varna, Bulgaria
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 97–102
- Language:
- URL:
- https://doi.org/10.26615/978-954-452-049-6_015
- DOI:
- 10.26615/978-954-452-049-6_015
- Cite (ACL):
- Victoria Bobicev and Marina Sokolova. 2017. Inter-Annotator Agreement in Sentiment Analysis: Machine Learning Perspective. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 97–102, Varna, Bulgaria. INCOMA Ltd..
- Cite (Informal):
- Inter-Annotator Agreement in Sentiment Analysis: Machine Learning Perspective (Bobicev & Sokolova, RANLP 2017)
- PDF:
- https://doi.org/10.26615/978-954-452-049-6_015