https://jjcit.org/paper/150 10.5455/jjcit.71-1632557439 Trieu Hai Nguyen,Thi-Kim-Ngoan Pham,Thi-Hong-Minh Bui,Thanh- Quynh- Chau Nguyen BERT,Clustering,Language models,Feature extraction,Word embeddings 2 5149 1192 26-Sep.-2021 10-Dec.-2021 28-Dec.-2021 The biggest challenge of building chatbots is training data. The required data must be realistic and large enough to train chatbots. We create a tool to get actual training data from Facebook messenger of a Facebook page. After text preprocessing steps, the newly obtained dataset generates FVnC and Sample dataset. We use the Retraining of BERT for Vietnamese (PhoBERT) to extract features of our text data. K-Means and DBSCAN clustering algorithms are used for clustering tasks based on output embeddings from PhoBERTbase. We apply V-measure score and Silhouette score to evaluate the performance of clustering algorithms. We also demonstrate the efficiency of PhoBERT compared to other models in feature extraction on the Sample dataset and wiki dataset. A GridSearch algorithm that combines both clustering evaluations is also proposed to find optimal parameters. Thanks to clustering such a number of conversations, we save a lot of time and effort to build data and storylines for training chatbot.