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Sentiment Analysis, Social Media and Urban Economics: The Case of Singaporean HDB and Covid-19

Author

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  • Srinaath Anbu Durai

    (Department of Information Systems and Operations Management, HEC Paris, Paris, France)

  • Wang Zhaoxia

    (School of Computing and Information Systems, Singapore Management University, Singapore)

Abstract

Twitter sentiment analysis has been employed as a prognostic tool for predicting prices and trends in both stock and housing markets. Early studies in this domain drew inspiration from behavioural economics, establishing a link between sentiments or emotions and economic decision-making. However, recent investigations in this field have shifted their focus from the data utilized to the algorithms employed. A comprehensive literature review, with an emphasis on the data aspect, reveals a scarcity of research considering the influence of sentiments arising from external factors on stock or housing markets, despite abundant evidence in behavioural economics suggesting that sentiments induced by external factors impact economic decisions. To bridge this gap, this study explores the impact of Twitter sentiment related to the Covid-19 pandemic on housing prices in Singapore. Employing SNSCRAPE for tweet collection, sentiment analysis is conducted using VADER. Granger Causality is applied to investigate the relationship between Covid-19 cases and sentiment, while neural networks serve as prediction models. The research compares the predictive capacity of Twitter sentiment regarding Covid-19 with traditional housing price predictors, such as structural and neighbourhood characteristics. Findings indicate that utilizing Twitter sentiment related to Covid-19 yields superior predictions compared to relying solely on traditional predictors, outperforming two specific traditional predictors. Consequently, this study underscores the significance of considering Twitter sentiment related to external factors as crucial in economic predictions, demonstrating practical applications of sentiment analysis on Twitter data in real-world economic scenarios.

Suggested Citation

  • Srinaath Anbu Durai & Wang Zhaoxia, 2023. "Sentiment Analysis, Social Media and Urban Economics: The Case of Singaporean HDB and Covid-19," International Journal of Innovation and Economic Development, Inovatus Services Ltd., vol. 9(5), pages 28-39, December.
    • Handle: RePEc:mgs:ijoied:v:9:y:2023:i:5:p:28-39
    DOI: 10.18775/ijied.1849-7551-7020.2015.95.2003
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    References listed on IDEAS

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    1. Genesove, David & Han, Lu, 2012. "Search and matching in the housing market," Journal of Urban Economics, Elsevier, vol. 72(1), pages 31-45.
    2. David Hirshleifer & Tyler Shumway, 2003. "Good Day Sunshine: Stock Returns and the Weather," Journal of Finance, American Finance Association, vol. 58(3), pages 1009-1032, June.
    3. Serda Selin Ozturk & Kursad Ciftci, 2014. "A Sentiment Analysis of Twitter Content as a Predictor of Exchange Rate Movements," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, vol. 6(2), pages 132-140, December.
    4. Jochen Hausler & Jessica Ruscheinsky & Marcel Lang, 2018. "News-based sentiment analysis in real estate: a machine learning approach," Journal of Property Research, Taylor & Francis Journals, vol. 35(4), pages 344-371, October.
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    More about this item

    Keywords

    Covid-19; Housing prices; Sentiment analysis; Twitter; Singapore;
    All these keywords.

    JEL classification:

    • M00 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - General - - - General

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