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Depression Analysis of Real Time Tweets During Covid Pandemic

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Ubiquitous Intelligent Systems (ICUIS 2021)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 302))

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Abstract

The assessment of depression and suicidal tendencies among people due to covid-19 was less explored. The paper presents the real-time framework for the assessment of depression in covid pandemic. This approach gives a better alternate option to reduce the suicidal tendency in covid time with retweeting and other alternate real-time ways. Hence, the main objective of the present work is, to develop a real time frame-work to analyse sentiment and depression in people due to covid. The experimental investigation is carried out based on real time streamed tweets from twitter adopting lexicon and machine learning (ML) approach. Linear regression, K-nearest neighbor (KNN), Naive Bayes models are trained and tested with 1000 tweets to ascertain the accuracy for the sentiment’s distribution. Comparatively, the decision tree (98.75%) and Naive Bayes (80.33%) have shown better accuracy with the visualisation of data to draw any inferences from sentiments using word cloud.

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Authors and Affiliations

  1. Department of ECE, BLDEAs V. P. Dr. P. G. Halakatti College of Engineering and Technology, Vijayapura, Karnataka, India

    G. B. Gour, Vandana S. Savantanavar,  Yashoda, Vijaylaxmi Gadyal & Sushma Basavaraddi

Authors
  1. G. B. Gour
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  2. Vandana S. Savantanavar
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  3. Yashoda
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  4. Vijaylaxmi Gadyal
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  5. Sushma Basavaraddi
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Correspondence to G. B. Gour .

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Editors and Affiliations

  1. Department of EEE, Shree Venkateshwara Hi-Tech Engg College, Gobichettipalayam, India

    P. Karuppusamy

  2. Castilla-La Mancha University, Ciudad Real, Spain

    Fausto Pedro García Márquez

  3. Department of Computer Science, Kennesaw State University, Marietta, GA, USA

    Tu N. Nguyen

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Gour, G.B., Savantanavar, V.S., Yashoda, Gadyal, V., Basavaraddi, S. (2022). Depression Analysis of Real Time Tweets During Covid Pandemic. In: Karuppusamy, P., García Márquez, F.P., Nguyen, T.N. (eds) Ubiquitous Intelligent Systems. ICUIS 2021. Smart Innovation, Systems and Technologies, vol 302. Springer, Singapore. https://doi.org/10.1007/978-981-19-2541-2_6

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