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classecol : Classifiers to understand public opinions of nature
Author(s) -
Johnson Thomas F.,
Kent Hebe,
Hill Bethan M.,
Dunn Georgia,
Dommett Leonie,
Penwill Natasha,
Francis Tom,
GonzálezSuárez Manuela
Publication year - 2021
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.13596
Subject(s) - classifier (uml) , computer science , data science , perception , artificial intelligence , domain (mathematical analysis) , social media , information retrieval , psychology , world wide web , mathematics , neuroscience , mathematical analysis
Human perceptions of nature, once the domain of the social sciences, are now an important part of environmental research. However, the data and tools to tackle this research are lacking or are difficult to apply. Here, we present a collection of text classifier models to identify text relevant to the broad topics of hunting and nature, describing whether opinions are pro‐ or against‐hunting, or show interest, concern or dislike of nature. The methods also include a biographical classification—describing whether the author of the text is a person, nature expert, nature organisation or ‘Other’. The classifiers were developed using an extensive social media dataset, and are designed to support qualitative analysis of big data (especially from Twitter). The classifiers accurately identified biographies, text related to hunting and nature and the stance towards hunting and nature (weighted F ‐scores: 0.79–0.99; 1 indicates perfect accuracy). These classifiers, alongside an array of other text processing and analysis functions, are presented in the form of an R package classecol . classecol also acts as a proof of concept that nature‐related text classifiers can be developed with high accuracy.