Understanding the expressive functions of jingju metrical patterns through lyrics text mining

Abstract

The emotional content of jingju (aka Beijing or Peking opera) arias is conveyed through pre-defined metrical patterns known as banshi, each of them associated with a specific expressive function. In this paper, we first report the work on a comprehensive corpus of jingju lyrics that we built, suitable for text mining and text analysis in a data-driven framework. Utilizing this corpus, we propose a novel approach to study the expressive functions of banshi by applying text analysis techniques on lyrics. First we apply topic modeling techniques to jingju lyrics text documents grouped at different levels according to the banshi they are associated with. We then experiment with several different document vector representations of lyrics in a series of document classification experiments. The topic modeling results showed that sentiment polarity (positive or negative) is better distinguished between different shengqiang-banshi (a more fine grained partition of banshi) than banshi alone, and we are able to achieve high accuracy scores in classifying lyrics documents into different banshi categories. We discuss the technical and musicological implications and possible future improvements.