预测US国家级农业情绪作为衡量粮食安全的一个指标，来自农业社区的推文
Predicting US State-Level Agricultural Sentiment as a Measure of Food Security with Tweets from Farming Communities
摘要：
在相关数据可能稀少的发展中地区，获得准确的粮食安全指标的能力对于负责实施粮食援助计划的政策制定者至关重要。因此，大量的工作致力于预测重要的粮食安全指标，如使用各种方法，包括模拟，遥感，天气模型和人类专家输入的年度作物产量。作为对现有作物产量预测技术的补充，这项工作开发了神经网络模型，用于预测来自农业社区的Twitter消息的情绪。具体来说，我们研究了在农业相关推文的小数据集上进行直接学习的潜力，以及从其他领域（例如，政治）来准确预测农业情绪，我们希望这最终将成为一个有用的作物产量预测器。我们发现，从小的相关数据集直接学习优于从大的完全标记的数据集迁移学习，卷积神经网络在Twitter情感分类上广泛优于递归神经网络，并且这些模型在实际设置的三元情感问题上的表现远不如文献中常见的二元问题。
英文摘要：
The ability to obtain accurate food security metrics in developing areas where relevant data can be sparse is critically important for policy makers tasked with implementing food aid programs. As a result, a great deal of work has been dedicated to predicting important food security metrics such as annual crop yields using a variety of methods including simulation, remote sensing, weather models, and human expert input. As a complement to existing techniques in crop yield prediction, this work develops neural network models for predicting the sentiment of Twitter feeds from farming communities. Specifically, we investigate the potential of both direct learning on a small dataset of agriculturally-relevant tweets and transfer learning from larger, well-labeled sentiment datasets from other domains (e.g.~politics) to accurately predict agricultural sentiment, which we hope would ultimately serve as a useful crop yield predictor. We find that direct learning from small, relevant datasets outperforms transfer learning from large, fully-labeled datasets, that convolutional neural networks broadly outperform recurrent neural networks on Twitter sentiment classification, and that these models perform substantially less well on ternary sentiment problems characteristic of practical settings than on binary problems often found in the literature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
