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Topic Modeling on Twitter Data and Identifying Health-Related Issues

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Information Management and Machine Intelligence (ICIMMI 2019)

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

The conversation over social media is becoming very common in the present world because of diverse use of mobile devices and availability of the Internet. This conversation on social media takes in the form of short text with some contextual words, and so, analyzing such short text is difficult. A Most common topic among social media user is health-related, which is difficult to identify and analyze. This study explores model for discovering health-related topics and issues from tweets. To demonstrate the topic modeling and its effectiveness, LDA along with the Topic Aspect Model method has been used and applied on tweets related to tobacco and alcohol use. The public health researcher can understand health-related issues through large conversational Twitter data.

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Author information

Authors and Affiliations

  1. Amity University, Noida, India

    Sandhya Avasthi

Authors
  1. Sandhya Avasthi
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Correspondence to Sandhya Avasthi .

Editor information

Editors and Affiliations

  1. Poornima Institute of Engineering and Technology, Jaipur, Rajasthan, India

    Dinesh Goyal

  2. Department of Automatics and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Bălaş

  3. CISCO Technologies, Milpitas, CA, USA

    Abhishek Mukherjee

  4. Universidade de Fortaleza, Fortaleza, Brazil

    Victor Hugo C. de Albuquerque

  5. Poornima Institute of Engineering and Technology, Jaipur, Rajasthan, India

    Amit Kumar Gupta

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Avasthi, S. (2021). Topic Modeling on Twitter Data and Identifying Health-Related Issues. In: Goyal, D., Bălaş, V.E., Mukherjee, A., Hugo C. de Albuquerque, V., Gupta, A.K. (eds) Information Management and Machine Intelligence. ICIMMI 2019. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-4936-6_6

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