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Topic modeling and sentiment analysis of global climate change tweets

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Abstract

Social media websites can be used as a data source for mining public opinion on a variety of subjects including climate change. Twitter, in particular, allows for the evaluation of public opinion across both time and space because geotagged tweets include timestamps and geographic coordinates (latitude/longitude). In this study, a large dataset of geotagged tweets containing certain keywords relating to climate change is analyzed using volume analysis and text mining techniques such as topic modeling and sentiment analysis. Latent Dirichlet allocation was applied for topic modeling to infer the different topics of discussion, and Valence Aware Dictionary and sEntiment Reasoner was applied for sentiment analysis to determine the overall feelings and attitudes found in the dataset. These techniques are used to compare and contrast the nature of climate change discussion between different countries and over time. Sentiment analysis shows that the overall discussion is negative, especially when users are reacting to political or extreme weather events. Topic modeling shows that the different topics of discussion on climate change are diverse, but some topics are more prevalent than others. In particular, the discussion of climate change in the USA is less focused on policy-related topics than other countries.

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Acknowledgements

This work was supported by National Science Foundation (Grant No. AGS 1560210) and South Carolina Research Foundation, University of South Carolina (USA) (Grant No. ASPIRE-I 13540-17-44820).

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Authors and Affiliations

  1. Department of Computer Science, Clemson University, Clemson, SC, USA

    Biraj Dahal

  2. Department of Mathematical Sciences, Clemson University, Clemson, SC, USA

    Biraj Dahal

  3. Department of Computing Sciences, Coastal Carolina University, Conway, SC, USA

    Sathish A. P. Kumar

  4. Department of Geography, University of South Carolina, Columbia, SC, USA

    Zhenlong Li

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  1. Biraj Dahal
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  2. Sathish A. P. Kumar
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  3. Zhenlong Li
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Correspondence to Sathish A. P. Kumar.

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Dahal, B., Kumar, S.A.P. & Li, Z. Topic modeling and sentiment analysis of global climate change tweets. Soc. Netw. Anal. Min. 9, 24 (2019). https://doi.org/10.1007/s13278-019-0568-8

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