The Power of Communities: A Text Classification Model with Automated Labeling Process Using Network Community Detection

TL;DR

This paper introduces a novel network community detection approach to automatically label text data, improving classification accuracy in machine learning models for various NLP applications.

Contribution

It presents a new method using community detection on sentence similarity networks for automatic labeling, enhancing text classification performance.

Findings

Community detection labels outperform human labels by 2.68-3.75% accuracy.

Support Vector Machine and Random Forest models benefit from network-based labels.

Method improves the development of more accurate conversational and text classification systems.

Abstract

Text classification is one of the most critical areas in machine learning and artificial intelligence research. It has been actively adopted in many business applications such as conversational intelligence systems, news articles categorizations, sentiment analysis, emotion detection systems, and many other recommendation systems in our daily life. One of the problems in supervised text classification models is that the models' performance depends heavily on the quality of data labeling that is 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 networks with sentences as the network nodes and pairwise cosine similarities between the Term Frequency-Inversed Document Frequency (TFIDF) vector representations of the sentences as the network link weights. We use the Louvain method to detect the communities in the sentence networks. We train and test the Support Vector Machine and the Random Forest models on both the human-labeled data and network community detection labeled data. Results showed that models with the data labeled by the network community detection outperformed the models with the human-labeled data by 2.68-3.75% of classification accuracy. Our method may help developments of more accurate conversational intelligence and other text classification systems.