@inproceedings{farzindar-jarvis-2024-nlp,
title = "{NLP} Analysis of Environmental Themes in Phish Lyrics Across Concert Locations and Years",
author = "Farzindar, Anna and
Jarvis, Jason",
editor = "Kruspe, Anna and
Oramas, Sergio and
Epure, Elena V. and
Sordo, Mohamed and
Weck, Benno and
Doh, SeungHeon and
Won, Minz and
Manco, Ilaria and
Meseguer-Brocal, Gabriel",
booktitle = "Proceedings of the 3rd Workshop on NLP for Music and Audio (NLP4MusA)",
month = nov,
year = "2024",
address = "Oakland, USA",
publisher = "Association for Computational Lingustics",
url = "https://aclanthology.org/2024.nlp4musa-1.5/",
pages = "25--30",
abstract = "This study delves into the application of advanced AI and natural language processing techniques (NLP), to analyze the lyrics of Phish, a renowned American jam band. Focusing on environmental themes within their extensive repertoire, this paper aims to uncover latent topics pertaining to environmental discourse, by using the topic modeling and environmental classifier. Through meticulous preprocessing, modeling, and interpretation, our findings shed light on the multifaceted portrayal of environmental issues in Phish`s lyrics. In this study, our primary contribution lies in lyrical analysis, as well as visualization and interpretation of the topics their lyrics cover, over the forty plus years the band has existed. Our lyrical visualizations aim to facilitate an understanding of how Phish selects the timing and location for their live performances in relation to the themes present in their music."
}
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<abstract>This study delves into the application of advanced AI and natural language processing techniques (NLP), to analyze the lyrics of Phish, a renowned American jam band. Focusing on environmental themes within their extensive repertoire, this paper aims to uncover latent topics pertaining to environmental discourse, by using the topic modeling and environmental classifier. Through meticulous preprocessing, modeling, and interpretation, our findings shed light on the multifaceted portrayal of environmental issues in Phish‘s lyrics. In this study, our primary contribution lies in lyrical analysis, as well as visualization and interpretation of the topics their lyrics cover, over the forty plus years the band has existed. Our lyrical visualizations aim to facilitate an understanding of how Phish selects the timing and location for their live performances in relation to the themes present in their music.</abstract>
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%0 Conference Proceedings
%T NLP Analysis of Environmental Themes in Phish Lyrics Across Concert Locations and Years
%A Farzindar, Anna
%A Jarvis, Jason
%Y Kruspe, Anna
%Y Oramas, Sergio
%Y Epure, Elena V.
%Y Sordo, Mohamed
%Y Weck, Benno
%Y Doh, SeungHeon
%Y Won, Minz
%Y Manco, Ilaria
%Y Meseguer-Brocal, Gabriel
%S Proceedings of the 3rd Workshop on NLP for Music and Audio (NLP4MusA)
%D 2024
%8 November
%I Association for Computational Lingustics
%C Oakland, USA
%F farzindar-jarvis-2024-nlp
%X This study delves into the application of advanced AI and natural language processing techniques (NLP), to analyze the lyrics of Phish, a renowned American jam band. Focusing on environmental themes within their extensive repertoire, this paper aims to uncover latent topics pertaining to environmental discourse, by using the topic modeling and environmental classifier. Through meticulous preprocessing, modeling, and interpretation, our findings shed light on the multifaceted portrayal of environmental issues in Phish‘s lyrics. In this study, our primary contribution lies in lyrical analysis, as well as visualization and interpretation of the topics their lyrics cover, over the forty plus years the band has existed. Our lyrical visualizations aim to facilitate an understanding of how Phish selects the timing and location for their live performances in relation to the themes present in their music.
%U https://aclanthology.org/2024.nlp4musa-1.5/
%P 25-30
Markdown (Informal)
[NLP Analysis of Environmental Themes in Phish Lyrics Across Concert Locations and Years](https://aclanthology.org/2024.nlp4musa-1.5/) (Farzindar & Jarvis, NLP4MusA 2024)
ACL