Responding to requests from citizens is an essential administrative service that affects the daily life of people. The drastic increase in the volume of citizen requests in recent years has necessitated on-going studies on the automatic classification of citizen requests due to the time, effort, and misclassification errors involved in manual classification. Even though there have been prior studies that have analyzed citizen requests according to topic and frequency, they ignore the complicated and dynamic nature of such a dataset. Using a deep learning algorithm, this study proposes an automatic classification model for unstructured data by using transportation-related citizen requests from January 15th, 2016 until November 7th, 2018 of the City of Boston, USA, as an example. A combination of unsupervised and supervised learning was applied to the data. To address the issue of imbalance in data, this study also considered an equalization method. Five stepwise models were applied to increase the classification accuracy for the unstructured data. The final model uses achieved a classification accuracy of 90%. The model proposed in this study is expected to be generalized for classification of other citizen requests or unstructured text data on specific topics in the future. Moreover, this study has substantial academic importance given that it has proven diverse machine learning-related theories through their application to unstructured data.

响应公民的要求是影响人们日常生活的一项基本行政服务。近年来，由于人工分类所涉及的时间，精力和错误分类错误，公民请求量的急剧增加使得有必要对公民请求的自动分类进行持续的研究。即使有以前的研究根据主题和频率分析了市民的要求，但他们忽略了这种数据集的复杂性和动态性。本研究使用深度学习算法，以2016年1月15日至2018年11月7日美国波士顿市与交通有关的市民要求为基础，提出了一种非结构化数据的自动分类模型。无监督学习和有监督学习的组合被应用于数据。为了解决数据不平衡的问题，本研究还考虑了一种均衡方法。应用了五个逐步模型来提高非结构化数据的分类精度。最终模型使用的分类精度达到90％。预期该研究中提出的模型将被推广用于将来对其他公民请求或特定主题的非结构化文本数据进行分类。此外，由于该研究已通过将其应用于非结构化数据而证明了与机器学习相关的各种理论，因此具有重要的学术意义。应用了五个逐步模型来提高非结构化数据的分类精度。最终模型使用的分类精度达到90％。预期该研究中提出的模型将被推广用于将来对其他公民请求或特定主题的非结构化文本数据进行分类。此外，由于该研究已通过将其应用于非结构化数据而证明了与机器学习相关的各种理论，因此具有重要的学术意义。应用了五个逐步模型来提高非结构化数据的分类精度。最终模型使用的分类精度达到90％。预期该研究中提出的模型将被推广用于将来对其他公民请求或特定主题的非结构化文本数据进行分类。此外，由于该研究已通过将其应用于非结构化数据而证明了与机器学习相关的各种理论，因此具有重要的学术意义。