Articolul precedent |
Articolul urmator |
459 41 |
Ultima descărcare din IBN: 2024-02-29 16:23 |
SM ISO690:2012 BOBICEV, Victoria, SOKOLOVA, Marina. Inter-annotator agreement in sentiment analysis: Machine learning perspective. In: Recent Advances in Natural Language Processing: RANLP, 2-8 septembrie 2017, Varna. Stroudsburg PA: Association for Computational Linguistics (ACL), 2017, Ediția 11, pp. 97-102. ISBN 978-954452048-9. DOI: https://doi.org/10.26615/978-954-452-049-6-015 |
EXPORT metadate: Google Scholar Crossref CERIF DataCite Dublin Core |
Recent Advances in Natural Language Processing Ediția 11, 2017 |
||||||
Conferința "11th International Conference on Recent Advances in Natural Language Processing" Varna, Bulgaria, 2-8 septembrie 2017 | ||||||
|
||||||
DOI:https://doi.org/10.26615/978-954-452-049-6-015 | ||||||
Pag. 97-102 | ||||||
|
||||||
Descarcă PDF | ||||||
Rezumat | ||||||
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. |
||||||
Cuvinte-cheie Artificial Intelligence, Classification (of information), Deep learning, Learning algorithms, sentiment analysis, text processing |
||||||
|