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Abstract

With the evolution of information technology, dramatic innovations in computerized mining techniques and opinion mining from SNS, especially sentiment analysis, have been gaining attention in predicting the feasibility of global environmental policy implementation. However, discussions on how to more effectively apply analytical results to strategy building for global biodiversity governance have yet to progress. This paper proposes a strategy-building framework for promoting pro-environmental behaviors on biodiversity conservation and attempts to extract sentiment information (especially fear, sadness, and anticipation) from posts on Twitter (X) in 2021 and 2022 using natural language processing (NLP). The results showed that sentiments of fear, sadness, and anticipation were prominent in both years regarding topics related to species extinction and decline, as well as the 30by30 initiative. Furthermore, based on the information on the sentiment-inducing factors identified from the analysis results, we presented practical examples of strategy building for pro-environmental behavior promotion, according to the framework.

Keywords

sentiment analysis biodiversity psychological theories Twitter natural language processing

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Deepening and Practical Application of Sentiment Analysis: Through Exploration of Public Interest and Sentiment on “Biodiversity” From Twitter

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