Sentiment Analysis Based on the BERT Model: Attitudes Towards Politicians Using Media Data
- DOI
- 10.2991/assehr.k.211218.007How to use a DOI?
- Keywords
- computational text analysis; targeted-BERT; tone dynamics; neural network; model training
- Abstract
The latest analysis methods of sentiments borrowed from computational linguistics are relevant in the age of big data, which is difficult to process through traditional content analysis. These methods have made it possible to analyze information over a long period, which allows us to trace the dynamics of the relationship to a particular object over time and large-scale comparative studies of texts. The authors demonstrate the applicability of sentiment analysis based on transformer models to the study of the temporal model of attitudes towards well-known politicians (2001-2021) on the example of text analysis of multilingual online publications. To do this, the authors used the targeted-BERT method for automated directed analysis of sentiments, obtained quality indicators F1-score 0.799 and 0.741 for Ukrainian and Russian models, respectively. The authors tested the dependence of mediatization of politicians on the country’s political hierarchy, confirmed hypotheses about the attitude to their power (more significant criticism of the Ukrainian media and gradual loyalty to the Russian media) and foreign politicians (dominance of negative tone in both media with a growing trend for Ukrainian media).
- Copyright
- © 2021 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Svitlana Salnikova AU - Roman Kyrychenko PY - 2021 DA - 2021/12/18 TI - Sentiment Analysis Based on the BERT Model: Attitudes Towards Politicians Using Media Data BT - Proceedings of the International Conference on Social Science, Psychology and Legal Regulation (SPL 2021) PB - Atlantis Press SP - 39 EP - 44 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.211218.007 DO - 10.2991/assehr.k.211218.007 ID - Salnikova2021 ER -