Text classification is one of the most critical areas of machine learning and artificial intelligence research. One of the problems in developing text classification models is that the performances of the models depend on the quality of labeling tasks that are typically done by humans. In this study, we propose a new network community detection-based approach to automatically label and classify text data into multiclass value spaces. Specifically, we build a network with sentences as network nodes and pairwise cosine similarities between sentences as link weights. We use the Louvain method  to group sentences into classes, and train Support Vector Machine  and Random Forest  models for classification using the community labels as part of the features. Results showed that models with the data labeled by network community detection outperformed the models with the human-labeled data by 2.68~3.75% classification accuracy increase on the test data provided by Pypestream. Our method may help development of a more accurate conversational intelligence system and other text classification systems.