Below are our references, a collection of both technical and historical papers, as well as published papers and blog posts.
Aristotle. (1906). The Nicomachean ethics of Aristotle. translated by F.H. peters. (F. H. Peters, Trans.) (10th ed.). Kegan Paul & Co.
Aristotle. (1944). Aristotle in 23 Volumes. (H. Rackham, Trans.) (Vol. 21). Harvard University Press.
Baron, A., & Rayson, P. (2008). VARD2:a tool for dealing with spelling variation in historical corpora. Postgraduate Conference in Corpus Linguistics.
Barua, A. (2021, June 17). Adding context to unsupervised sentiment analysis. Medium. https://medium.com/swlh/adding-context-to-unsupervised-sentiment-analysis-7b6693d2c9f8.
García-Pablos, A., Cuadros, M., & Rigau, G. (2018). W2vlda: Almost unsupervised system for aspect based sentiment analysis. Expert Systems with Applications, 91, 127–137. https://doi.org/10.1016/j.eswa.2017.08.049
History of the wool trade. Historic UK. (n.d.). https://www.historic-uk.com/HistoryUK/HistoryofEngland/Wool-Trade/.
Holtz, Y. (n.d.). Boxplot. the R Graph Gallery. https://www.r-graph-gallery.com/boxplot.html.
Jervis, B. (2017). Consumption and The ‘SOCIAL Self’ in MEDIEVAL Southern England. Norwegian Archaeological Review, 50(1), 1–29. https://doi.org/10.1080/00293652.2017.1326978
Julia Silge. (2018, January 25). The game is afoot! topic modeling of Sherlock Holmes stories. Julia Silge. https://juliasilge.com/blog/sherlock-holmes-stm/.
Koncar, P., Fuchs, A., Hobisch, E., Geiger, B. C., Scholger, M., & Helic, D. (2020). Text sentiment in the age of enlightenment: An analysis of spectator periodicals. Applied Network Science, 5(1). https://doi.org/10.1007/s41109-020-00269-z
Li, S. (2018, June 1). Topic modeling and latent dirichlet allocation (lda) in python. Medium. https://towardsdatascience.com/topic-modeling-and-latent-dirichlet-allocation-in-python-9bf156893c24.
Mei, T. S. (2009). The preeminence of use: Reevaluating the relation between use and exchange in aristotle’s economic thought. Journal of the History of Philosophy, 47(4), 523–548. https://doi.org/10.1353/hph.0.0168
NLPiation. (2021, April 23). Is it possible to do sentiment analysis on unlabeled data Using BERT? (Feat. Vader) [Experiment]. Medium. https://nlpiation.medium.com/is-it-possible-to-do-sentiment-analysis-on-unlabeled-data-using-bert-feat-vader-experiment-357bba53768c.
Pogiatzis, A. (2019, March 20). Nlp: Contextualized word embeddings from bert. Medium. https://towardsdatascience.com/nlp-extract-contextualized-word-embeddings-from-bert-keras-tf-67ef29f60a7b.
Shafi, A. (2021, March 29). Upgrade your beginner nlp project with bert. Medium. https://towardsdatascience.com/text-classification-with-bert-2e0297ea188a.
Vries, D. J. (2009). The industrious revolution: Consumer behavior and the Household Economy, 1650 to the present. Cambridge University Press.
Withington, P. (2020). Intoxicants and the invention of ‘consumption.’ The Economic History Review, 73(2), 384–408. https://doi.org/10.1111/ehr.12936
WITHINGTON, P. H. I. L. (2011). INTOXICANTS and society in early modern england. The Historical Journal, 54(3), 631–657. https://doi.org/10.1017/s0018246x11000197
Wojcik, R. (2020, March 2). Unsupervised sentiment analysis. Medium. https://towardsdatascience.com/unsupervised-sentiment-analysis-a38bf1906483.
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