  This paper presents a hybrid machine learning method of classifying
residential requests in natural language to responsible departments that
provide timely responses back to residents under the vision of digital
government services in smart cities. Residential requests in natural language
descriptions cover almost every aspect of a city's daily operation. Hence the
responsible departments are fine-grained to even the level of local
communities. There are no specific general categories or labels for each
request sample. This causes two issues for supervised classification solutions,
namely (1) the request sample data is unbalanced and (2) lack of specific
labels for training. To solve these issues, we investigate a hybrid machine
learning method that generates meta-class labels by means of unsupervised
clustering algorithms; applies two-word embedding methods with three
classifiers (including two hierarchical classifiers and one residual
convolutional neural network); and selects the best performing classifier as
the classification result. We demonstrate our approach performing better
classification tasks compared to two benchmarking machine learning models,
Naive Bayes classifier and a Multiple Layer Perceptron (MLP). In addition, the
hierarchical classification method provides insights into the source of
classification errors.
