This dissertation examines selected love lyrics composed during the 1930s by Cole Porter, whose witty and urbane music epitomized the Golden era of American light music. These lyrics present an interesting paradox – a man who longed for his music to be accepted by the American public, yet remained indifferent to the social mores of the time. Porter offered trenchant social commentary aimed at a society restricted by social taboos and cultural conventions. The argument develops systematically through a chronological and contextual study of the influences of people and events on a man and his music. The prosodic intonation and imagistic texture of the lyrics demonstrate an intimate correlation between personality and composition which, in turn, is supported by the biographical content. / English / M.A. (English)

This dissertation examines selected love lyrics composed during the 1930s by Cole Porter, whose witty and urbane music epitomized the Golden era of American light music. These lyrics present an interesting paradox – a man who longed for his music to be accepted by the American public, yet remained indifferent to the social mores of the time. Porter offered trenchant social commentary aimed at a society restricted by social taboos and cultural conventions. The argument develops systematically through a chronological and contextual study of the influences of people and events on a man and his music. The prosodic intonation and imagistic texture of the lyrics demonstrate an intimate correlation between personality and composition which, in turn, is supported by the biographical content. / English / M.A. (English)

The lyrics of Sammy Cahn played a dominant role in shaping the Golden Age of American light music. He remains the most successful lyricist in cinema history, in terms of Academy Awards and Nominations, yet he has received little acclaim for his achievements. This thesis explores the diverse constituents of his creative genius, focusing on his ability to “romance the vernacular”, and write “bespoke” material on request. The argument follows a chronological path, tracing the major influences on Cahn’s life: vaudeville and musical theatre, the growth of the film industry, and the collaborators and performers who helped him achieve a level of mastery that he sustained for nearly fifty years. Particular emphasis is placed on his relationship with Frank Sinatra, on both a personal and professional level. Cahn had an acute awareness of the human condition and his ability to convey a range of emotions to match mood and moment displayed consummate craft and intellect, with a self-confidence that bordered on bravado. His contemporaries in the Golden Age of popular song have received due recognition, yet little has been written about Cahn, whose appreciation of the interaction between spontaneity and creativity remains unsurpassed by fellow lyricists. He had an intuitive understanding of the vernacular and an instinctive ability to write to order. The imagistic texture of the lyrics coupled with the prosodic intonation demonstrate an intimate correlation between personality and composition which is supported by biographical content. The argument, augmented by an audio-documentary, develops systematically through a study of the lyrics, focusing on the cultural and musicological significance of Cahn’s oeuvre. The material for both the written text and the two accompanying CDs are from personal archives and the Margaret Herrick Library in Los Angeles, which is the repository for the Sammy Cahn Collection, bequeathed to that institution after the death of Cahn in 1993. / English Studies / D. Litt. et Phil. (English)

Sentiment Analysis is an area of Computer Science that deals with the impact a document makes on a user. The very field is further sub-divided into Opinion Mining and Emotion Analysis, the latter of which is the basis for the present work. Work on songs is aimed at building affective interactive applications such as music recommendation engines. Using song lyrics, we are interested in both supervised and unsupervised analyses, each of which has its own pros and cons. For an unsupervised analysis (clustering), we use a standard probabilistic topic model called Latent Dirichlet Allocation (LDA). It mines topics from songs, which are nothing but probability distributions over the vocabulary of words. Some of the topics seem sentiment-based, motivating us to continue with this approach. We evaluate our clusters using a gold dataset collected from an apt website and get positive results. This approach would be useful in the absence of a supervisor dataset. In another part of our work, we argue the inescapable existence of supervision in terms of having to manually analyse the topics returned. Further, we have also used explicit supervision in terms of a training dataset for a classifier to learn sentiment specific classes. This analysis helps reduce dimensionality and improve classification accuracy. We get excellent dimensionality reduction using Support Vector Machines (SVM) for feature selection. For re-classification, we use the Naive Bayes Classifier (NBC) and SVM, both of which perform well. We also use Non-negative Matrix Factorization (NMF) for classification, but observe that the results coincide with those of NBC, with no exceptions. This drives us towards establishing a theoretical equivalence between the two.