Exploring Human Emotions for Depression Detection from Twitter Data by Reducing Misclassification Rate

The long-held notion that Bartók’s style represents a unique synthesis of features derived from folk music, from the works of his best contemporaries, as well as from the great classical masters has resulted in a certain asymmetry in Bartók studies. This article provides a short overview of the debate concerning the “Bartókian synthesis,” and presents a case study to illuminate how an ostensibly “lesser” historical figure like Domenico Scarlatti could have proved important for Bartók in several respects. I suggest that it must almost certainly have been Sándor Kovács who called Scarlatti’s music to Bartók’s attention around 1910, and so Kovács’s 1912 essay on the Italian composer may tell us much about Bartók’s Scarlatti reception as well. I argue that, while Scarlatti’s musical style may indeed have appealed to Bartók in more respects than one, he may also have identified with Scarlatti the man, who (in Kovács’s interpretation) developed a thoroughly ironic style in response to the unavoidable loneliness that results from the impossibility of communicating human emotions (an idea that must have intrigued Bartók right around the time he composed his Duke Bluebeard’s Castle ). In conclusion I propose that Scarlatti’s Sonata in E major (L21/K162), which Bartók performed on stage and also edited for an instructive publication, may have inspired the curious structural model that found its most clear-cut realization in Bartók’s Third Quartet.

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Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

Fieldwork in Religion ◽

10.1558/firn.40610 ◽

2020 ◽

Vol 14 (2) ◽

pp. 140-159

Author(s):

Anthony-Paul Cooper ◽

Emmanuel Awuni Kolog ◽

Erkki Sutinen

Keyword(s):

Machine Learning ◽

Online Community ◽

High Volume ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Social Media Data ◽

Twitter Data ◽

Resource Intensity ◽

Media Data ◽

Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

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