Analysis of the Arab singer Shadia’s lyrics

Abstract In this work, we analyze the lyrics of one of the most famous and influential Arab artists in the twentieth century, namely "Image missing" (Shadia). Lyrics analysis provides a deep insight into the artist’s career evolution and her interactions with the surrounding environment including the social, political, and economic conditions. In order to perform such analysis, we had to collect and compile the lyrics of Shadia accompanied with the necessary meta-data into an organized and structured form. The data are preprocessed by removing stop words and doing some normalization operations over the songs prose. We did not perform any lemmatization or stemming as the original form of the tokens conveys much more information than the source words. We performed a lexical analysis in order to study both the lexical diversity and density over the course of Shadia’s career life. We have as well studied the most significant words, idioms, and terms played in the songs using tools such as word clouds and more quantitative measures such as term frequency–inverse document frequency. We have divided the career life of Shadia into sub-decades of length 5 years, and all analyses are done both in a yearly fashion and more coarsely over such sub-decades. Our quantitative analyses show strong correlations between the artistic lyrical work of Shadia and the state of affairs in Egypt and the Arab World during her time. In particular, Shadia’s lyrics reflect the radical changes in all aspects of the social, political, and economic conditions. This is especially relevant knowing that Shadia is very much truly considered the daughter of the generation of the 1952 revolution in Egypt. The significance of Shadia and her lyrics stem essentially from being contemporaneous to radical changes in Egypt across all sectors including political (support of liberation movements across the world and the conflict with Israel) and socioeconomic (especially changing the social class structure in Egypt). We also investigated the potential effectiveness of PoS (Part-of-Speech) tagging in genre analysis and classification.