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Sustainable Consumption in Consumer Behavior in the Time of COVID-19: Topic Modeling on Twitter Data Using LDA

Author

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  • Paweł Brzustewicz

    (Department of Organizational Behavior and Marketing, Faculty of Economic Sciences and Management, Nicolaus Copernicus University, 87-100 Toruń, Poland)

  • Anupam Singh

    (Department of Organizational Behavior and Marketing, Faculty of Economic Sciences and Management, Nicolaus Copernicus University, 87-100 Toruń, Poland)

Abstract

By using text mining techniques, this study identifies the topics of sustainable consumption that are important during the COVID-19 pandemic. An Application Programming Interface (API) streaming method was used to extract the data from Twitter. A total of 14,591 tweets were collected using Twitter streaming API. However, after data cleaning, 13,635 tweets were considered for analysis. The objectives of the study are to identify (1) the topics users tweet about sustainable consumption and (2) to detect the emotion-based sentiments in the tweets. The study used Latent Dirichlet Allocation (LDA) algorithm for topic modeling and the Louvain algorithm for semantic network clustering. NRC emotion lexicon was used for sentiment analysis. The LDA model discovers six topics: organic food consumption, food waste, vegan food, sustainable tourism, sustainable transport, and sustainable energy consumption. While the Louvain algorithm detects four clusters—lifestyle and climate change, responsible consumption, energy consumption, and renewable energy, sentiment analysis results show more positive emotions among the users than the negative ones. The study contributes to existing literature by providing a fresh perspective on various interconnected topics of sustainable consumption that bring global consumption to a sustainable level.

Suggested Citation

  • Paweł Brzustewicz & Anupam Singh, 2021. "Sustainable Consumption in Consumer Behavior in the Time of COVID-19: Topic Modeling on Twitter Data Using LDA," Energies, MDPI, vol. 14(18), pages 1-20, September.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5787-:d:635012
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    2. Martin Zajac & Jiří Horák & Joaquín Osorio-Arjona & Pavel Kukuliač & James Haworth, 2022. "Public Transport Tweets in London, Madrid and Prague in the COVID-19 Period—Temporal and Spatial Differences in Activity Topics," Sustainability, MDPI, vol. 14(24), pages 1-25, December.
    3. Čiarnienė Ramunė & Vienažindienė Milita & Adamonienė Rūta, 2023. "Teleworking and sustainable behaviour in the context of COVID-19: the case of Lithuania," Engineering Management in Production and Services, Sciendo, vol. 15(1), pages 1-11, March.
    4. Leslier Valenzuela-Fernández & Manuel Escobar-Farfán & Mauricio Guerra-Velásquez & Elizabeth Emperatriz García-Salirrosas, 2023. "COVID-19 Effects on Environmentally Responsible Behavior: A Social Impact Perspective from Latin American Countries," IJERPH, MDPI, vol. 20(4), pages 1-21, February.
    5. Barbara Zanetti & Milena Verrascina & Francesco Licciardo & Giuseppe Gargano, 2022. "Agritourism and Farms Diversification in Italy: What Have We Learnt from COVID-19?," Land, MDPI, vol. 11(8), pages 1-21, August.
    6. Eleonora Annunziata & Tommaso Pucci & Jacopo Cammeo & Lorenzo Zanni & Marco Frey, 2023. "The mediating role of exogenous shocks in green purchase intention: evidence from italian fashion industry in the Covid-19 era," Italian Journal of Marketing, Springer, vol. 2023(1), pages 59-79, March.

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