![]() ![]() We collect all words that appear with a specific chord and create a large ‘bag of words’-a frequency list of words-for each chord ( figure 1). Here, we apply this sentiment analysis method to a dataset of guitar tablatures-which contain both lyrics and chords-extracted from. This method has been successfully used to obtain insight into a wide variety of corpora. The overall valence of a piece of text, such as a sentence or document, is measured by averaging the valence score of individual words within the text. The lexicon contains valence scores ranging from 0.0 (saddest) to 9.0 (happiest) for 10 222 common English words obtained by surveying Amazon’s Mechanical Turk workers. Valence is one of the two basic emotional axes, with higher valence corresponding to more attractive/positive emotion. To do so, we adopt a sentiment analysis method that uses the crowd-sourced LabMT 1.0 valence lexicon. Major and Minor) and their associations with sentiment and words across genres, regions and eras. In particular, we use sentiment analysis to analyse chord categories (e.g. Here, we propose a novel method to study the associations between chord types and emotional valence. There has been also an attempt to examine the associations between lyrics and individual chords using a machine translation approach, which confirmed the notion that Major and Minor chords are associated with happy and sad words respectively. It has been shown that the lexical features from lyrics, metadata, social tags and audio-based features can be used to predict the mood of a song. For instance, sentiment analysis has been applied to uncover a long-term trend of declining valence in popular song lyrics. Meanwhile, the growth of music databases as well as the advancement of the field of Music Information Retrieval (MIR) opened new avenues for data-driven studies of music. ![]() As a result, creating large datasets and discovering statistical regularities has been a challenge. Such methods may produce high-quality data, but the data collection process involved is both labour- and resource-intensive. ![]() The study of music perception has been dominated by methods that directly measure emotional responses, such as self-reports, physiological and cognitive measurements, and developmental observations. Although music has accompanied humanity since the dawn of culture and its underlying mathematical structure has been studied for many years, understanding the link between music and emotion remains a challenge. The power of music to evoke strong feelings has long been admired and explored. ![]()
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