Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
How Did English Songs Evolve? Retrieving Information from Song Lyrics Via Natural Language Processing and Statistical Topic Modeling

How Did English Songs Evolve? Retrieving Information from Song Lyrics Via Natural Language Processing and Statistical Topic Modeling

Zapf, Laura

Paperback

Business GeneralProbability & Statistics

ISBN10: 3346376257
ISBN13: 9783346376251
Publisher: Grin Verlag
Published: May 26 2021
Pages: 114
Weight: 0.35
Height: 0.27 Width: 5.83 Depth: 8.27
Language: English
Master's Thesis from the year 2019 in the subject Mathematics - Statistics, grade: 1,0, University of Bamberg (Statistik und Ökonometrie), language: English, abstract: In this thesis, the similarity, the complexity, as well as the evolution of English song lyrics over the past five decades will be examined with the help of statistical methods. Hence, the central research question of this thesis is: Can information gained by Natural Language Processing and statistical topic modelling be used to determine whether and to what extent song lyrics of various genres changed over the course of the past 50 years? Based on this, the goals of this thesis are: determining how similar songs of five diverse genres (alternative, country, pop, rock, and hip-hop) are, as measured by text statistics and text features that are composed by Natural Language Processing (NLP) and text mining methods. Additionally, using these methods as well as an attempt to find out whether song lyrics are becoming less complex and therefore less sophisticated And, finally, the main target of this thesis set for itself, computing statistical topic models by applying Latent Dirichlet Allocation (LDA), to analyse how similar the topics of songs are and whether they changed over time. This will be conducted by calculating similarity measures on the per-topic-per-word probability distributions that are output of the LDA models.

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