In the United States, the conservation of federal lands reflects a social history of public advocacy, public policy, and public comments. US federal agencies solicit public comments to scope for ideas, solve problems, and use the best available science for policy‐making, legislation, and management. Online comment submission has led to staggering numbers of comments that are challenging to summarize. Here, we analyze comments received by the Department of the Interior in response to the proposed executive review of 27 national monuments designated and expanded between 1996 and 2016. We used a deep recurrent neural network (AWD‐LSTM) to classify sentiment of 754,707 comments with higher precision and recall (F1‐score = 0.98) than support vector machine and Naïve Bayes approaches. Over 97% of unique comments opposed the executive review, suggesting overwhelming support for maintaining national monument designations. Using cosine similarity, we also found that duplicates or potential automated software bots comprised over two‐thirds of comments. We offer recommendations for comment submission, collection, and analysis in the current techno‐political climate.

中文翻译：

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递归神经网络揭示了压倒性的情绪，反对2017年人类和机器人对美国古迹的评论

在美国，联邦土地的保护反映了公众倡导，公共政策和公众意见的社会历史。美国联邦机构征求公众意见，以求取意见范围，解决问题，并使用最佳科学方法进行决策，立法和管理。在线评论提交导致难以概括的评论数量之多。在这里，我们分析内政部收到的评论，以回应对1996年至2016年间指定和扩展的27个国家历史遗迹的拟议行政审查。我们使用深度递归神经网络（AWD-LSTM）将754,707条评论的情感分类为比支持向量机和朴素贝叶斯方法具有更高的精度和召回率（F1分数= 0.98）。超过97％的独特评论反对执行审查，建议为维持国家古迹名称提供压倒性支持。使用余弦相似度，我们还发现重复项或潜在的自动化软件漫游器占了三分之二以上的评论。在当前的技术政治环境中，我们提供了有关意见提交，收集和分析的建议。