Text Classification Using XLNet with Infomap Automatic Labeling Process

Text data is growing rapidly and used in various fields such as chatbots and question answering systems, which are currently popular, where the system identifies the question category and the possibility of an answer to help provide answers to the questions entered. Having good quality text data, especially in text classification, significantly affects the performance of the model. Manual labeling by humans, generally used in labeling training data in supervised learning, is expensive, prone to mistakes, and has a low quantity. Automatic labeling that providing high quality and high quantity of training data is necessary to improve text classification performance. This study attempts to conduct community detection with the Infomap algorithm for automatic labeling in text classification using XLNet. The accuracy of the model is compared to the baseline, which using data with manual labeling. While the accuracy has not outperformed the overall baseline yet, but the result shows that automatic labeling can improve data labeling quickly with high quantity.