Version 1
: Received: 22 April 2023 / Approved: 29 April 2023 / Online: 29 April 2023 (08:14:03 CEST)
How to cite:
Zipaer, M.; Yoshida, M.; Matumoto, K.; Kita, K. Using Machine Learning to Information Classification Related on Child-Rearing of Infants from Twitter. Preprints2023, 2023041230. https://doi.org/10.20944/preprints202304.1230.v1
Zipaer, M.; Yoshida, M.; Matumoto, K.; Kita, K. Using Machine Learning to Information Classification Related on Child-Rearing of Infants from Twitter. Preprints 2023, 2023041230. https://doi.org/10.20944/preprints202304.1230.v1
Zipaer, M.; Yoshida, M.; Matumoto, K.; Kita, K. Using Machine Learning to Information Classification Related on Child-Rearing of Infants from Twitter. Preprints2023, 2023041230. https://doi.org/10.20944/preprints202304.1230.v1
APA Style
Zipaer, M., Yoshida, M., Matumoto, K., & Kita, K. (2023). Using Machine Learning to Information Classification Related on Child-Rearing of Infants from Twitter. Preprints. https://doi.org/10.20944/preprints202304.1230.v1
Chicago/Turabian Style
Zipaer, M., Kazuyuki Matumoto and Kenji Kita. 2023 "Using Machine Learning to Information Classification Related on Child-Rearing of Infants from Twitter" Preprints. https://doi.org/10.20944/preprints202304.1230.v1
Abstract
It is difficult to obtain necessary information accurately from Social Networking Service (SNS) while raising children, and it is thought that there is a certain demand for the development of a system that presents appropriate information to users according to the child's developmental stage. There are still few examples of research on knowledge extraction that focuses on childcare. This research aims to develop a system that extracts and presents useful knowledge for people who are actually raising children, using texts about childcare posted on Twitter. In many systems numbers in text data are just strings like words and are normalized to zero or simply ignored. In this paper, we created a set of tweet texts and a set of profiles created according to the developmental stages of infants from "0-year-old child" to "6-year-old child". For each set, we used Support Vector Machine (SVM), and Bidirectional Encoder Representations from Transformers (BERT), a neural language model, to construct a classification model that predicts numbers from "0" to "6" from sentences. The accuracy rate predicted by the BERT classifier was slightly higher than that of the SVM classifier, indicating that the BERT classification method was better.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
