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Exploiting Topic Modelling to Classify Sentiment from Lyrics

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Machine Learning, Image Processing, Network Security and Data Sciences (MIND 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1241))

Abstract

With the increase in an enormous amount of data, text analysis has become a challenging task. Techniques like classification, categorization, summarization and topic modeling have become part of every natural language processing activity. In this experiment, we aim to perform sentiment class extraction from lyrics using topic modeling techniques. We have use generative statistical model Latent Dirichlet Allocation (LDA) which is also the most widely explored model in topic modeling and another nonparametric bayesian based approach model Heuristic Dirichlet Process (HDP) to extract the topics from 150 lyrics samples of Manipuri songs written using Roman script. We observe this unsupervised techniques, able to obtain the underlying different sentiment class of lyrics in the form of topics.

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Authors and Affiliations

  1. IIIT Manipur, Imphal, India

    Maibam Debina Devi & Navanath Saharia

Authors
  1. Maibam Debina Devi
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  2. Navanath Saharia
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Corresponding author

Correspondence to Maibam Debina Devi .

Editor information

Editors and Affiliations

  1. National Institute of Technology Silchar, Silchar, India

    Arup Bhattacharjee

  2. National Institute Of Technology Silchar, Silchar, India

    Samir Kr. Borgohain

  3. National Institute of Technology Silchar, Silchar, India

    Badal Soni

  4. National Institute of Technology Kurukshetra, Kurukshetra, India

    Gyanendra Verma

  5. University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

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Devi, M.D., Saharia, N. (2020). Exploiting Topic Modelling to Classify Sentiment from Lyrics. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1241. Springer, Singapore. https://doi.org/10.1007/978-981-15-6318-8_34

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  • DOI: https://doi.org/10.1007/978-981-15-6318-8_34

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-6317-1

  • Online ISBN: 978-981-15-6318-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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