Research Article
An Application of Non Negative Matrix Factorization in Text Mining
@INPROCEEDINGS{10.1007/978-3-031-47359-3_21, author={Nguyen Bao Tran and Thanh Son Huynh and Ba Lam To and Luong Anh Tuan Nguyen}, title={An Application of Non Negative Matrix Factorization in Text Mining}, proceedings={Industrial Networks and Intelligent Systems. 9th EAI International Conference, INISCOM 2023, Ho Chi Minh City, Vietnam, August 2-3, 2023, Proceedings}, proceedings_a={INISCOM}, year={2023}, month={10}, keywords={NMF text mining topic classification bags-of-words}, doi={10.1007/978-3-031-47359-3_21} }- Nguyen Bao Tran
Thanh Son Huynh
Ba Lam To
Luong Anh Tuan Nguyen
Year: 2023
An Application of Non Negative Matrix Factorization in Text Mining
INISCOM
Springer
DOI: 10.1007/978-3-031-47359-3_21
Abstract
The field of text mining has increasingly relied on Non-negative matrix factorization (NMF) for its ability to perform high-dimensional data reduction and visualization. This paper aims to employ NMF in analyzing a dataset of 1,500 documents and 12,419 words in bags-of-words format, obtained from the UCI Machine Learning Repository. Our analysis demonstrates the utility of NMF in effectively classifying ambiguous and sparse textual data into distinct topics and extracting meaningful contents through the identification of relevant keywords. Further, we demonstrate the robustness of NMF in topic clustering by exploring the semantic relationship between extracted keywords and the topics to which they belonged. Our findings offered valuable insights into the application of NMF in text mining and suggested that universities in Vietnam could leverage this technique to analyze feedback and suggestions from students.
