Peisistratos and the Chariot of Athena Episode. A Chronological Reappraisal
Abstract
The episode of Peisistratos going back to Athens in a chariot with Athena is one of the best-known events of the Greek archaic period. Following Herodotus and Aristotle, the current modern historiography places it after his first exile and before he gets his second tyranny. This consideration raises up a series of problems, like the role of Athena in a prenuptial agreement. For this reason, through a detailed study of the sources, as well as the historical context, we propose to delay this dramatized entry to 546, when the battle of Palene occurs and shortly before Peisistratos was installed as a tyrant for the third and last time. This new chronology better supports some interpretations that have been already given, but also allows, in turn, novel readings on the means of religious and social legitimation used by the tyrant of Athens.
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Elghazaly, T. Mahmoud, A. Hefny, H. A., 2016. Political sentiment analysis using twitter data. In: Proceedings of the International Conference on Internet of things and Cloud Computin,1-5.
Elmas, Ç., 2003. Yapay Sinir Ağları (Kuram, Mimari, Eğitim, Uygulama). Ankara: Seçkin Yayıncılık.
Harrington, P.,2012. Machine learning in action. Shelter Island, NY: Manning Publications Co.
Hamoud, A. A., Alwehaibi, A., Roy, K., and Bikdash, M. 2018. Classifying political tweets using Naïve Bayes and support vector machines. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems(736-744). Springer, Cham.
Huq, M. R., Ali, A., and Rahman, A., 2017. Sentiment analysis on Twitter data using KNN and SVM. (IJACSA) International Journal of Advanced Computer Science and Applications, 8(6): 19-25.
Kayikci, S., Akyazi, E., 2018. Classification of Open Directory Web Pages Using Artificial Neural Networks. International Journal of Scientific and Technological Research, 2422-8702
Kaynar, O., Görmez, Y., Yıldız, M., and Albayrak, A., 2016 .Makine öğrenmesi yöntemleri ile Duygu Analizi. In International Artificial Intelligence and Data Processing Symposium (IDAP’16), 17-18.
Kotzias, D., Denil, M., De Freitas, N., and Smyth, P. 2015. From group to individual labels using deep features. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 597-606.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J., 2013. Distributed compositionality. Advances in Neural Information Processing Systems. 26: 3111-3119.
Nikfarjam, A, Sarker, A, O’Connor, K, Ginn, R, and Gonzalez, G., 2015. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features, Journal of the American Medical Informatics Association, 22(3): 671-681
Nizam, H, Akın, S. S.,2014. Sosyal medyada makine öğrenmesi ile duygu analizinde dengeli ve dengesiz veri setlerinin performanslarının karşılaştırılması. XIX. Türkiye’de İnternet Konferansı.
Pang, B., Lee, L., and Vaithyanathan, S. 2002. Thumbs up? Sentiment classification using machine learning techniques. arXiv preprint cs/0205070.
Rana, S. and Singh, A.,2016. Comparative analysis of sentiment orientation using SVM and Naïve Bayes techniques, 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, pages 106-111, doi: 10.1109/NGCT.2016.7877399.
Rogers, R., 2014. Debanalising Twitter. Twitter and Society, New York, NY, ix-xxxviii.
Sjögren, R., Stridh, K., Skotare, T., and Trygg, J., 2020. Multivariate patent analysis–Using chemometrics to analyze collections of chemical and pharmaceutical patents. Journal of Chemometrics, 34(1): e3041.
Song, O., Hu, W., and Xie, W, 2002. Robust Support Vector Machine with Bullet Hole Image Classification, IEEE Transactions on Systems, Man and Cybernetics – Part C: Applications and Rewiews, 32(4): 440-448.
Symeonidis S, Effrosynidis D., and Arampatzis A., 2002. A comparative evaluation of pre‐processing techniques and their interactions for Twitter sentiment analysis. Expert System Applications, 110:298‐310.
Türkmen, A. C. Cemgil, A. T., 2014. Political interest and tendency prediction from microblog data. In: 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 1327-1330
Wright, G., Rodriguez, A., Li, J., Clark, P. L., Milenković, T., and Emrich, S. J., 2020. Analysis of computational codon usage models and their association with translationally slow codons. PloS one, 15(4): e0232003.
Xiao, C., Xia, W., and Jiang, J., 2020. Stock price forecast based on combined model of ARI-MA-LS-SVM. Neural Computing and Applications, 1-10.
Peisistratos and the Chariot of Athena Episode. A Chronological Reappraisal. (2021). Studia Historica. Historia Antigua, 39, 51-72. https://doi.org/10.14201/shha2021395172
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