Automated detection and correlation of social issues in the music of the 2000s (using the example of Russia and France)

Student: Aleksey Makovskiy

Supervisor: Anastasiya A. Bonch-Osmolovskaya

Educational Programme: Digital Humanities (Master)

Year of Graduation: 2024

The master's thesis is devoted to the study and comparison of the themes of song lyrics in French and Russian. Based on the collected dataset, an attempt is made to identify social issues from the texts using “distant reading” methods. The dataset under consideration covers the period of the first decade of the 2000s and includes more than 8.5 thousand songs in Russian and more than 23 thousand songs in French. The composition of the dataset was analyzed and genre trends were identified. Key words and topics were identified, and clustering was performed using the TF-IDF + K-means and LDA + pyLDAvis package algorithms implemented in Python. After counting and interpreting the results, the topics were compared and a conclusion was made about their similarity.

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