Text classification based on machine learning for Tibetan social network
Publication Name
Proceedings - 2022 10th International Conference on Advanced Cloud and Big Data, CBD 2022
Abstract
Social network technologies have gained widespread attention in many fields. However, the research on Tibetan Social Network (TSN) is limited to the sentiment analysis of micro-blogs, and few researchers focus on text classification and data mining in TSN. It cannot meet the social needs of the majority of Tibetans and the text information they really care about. In this paper, we investigate and compare different models that we adopted for the classification of Tibetan text. Machine learning models including Naive Bayesian (NB), Random Forest (RF), Support Vector Machine (SVM), fastText and text Convolutional Neural Networks (CNN) are used as classifiers to determine the best approach in Tibetan Social Network. In addition, term frequency-inverse document frequency (TF-IDF) is used to extract hot words and generate the word cloud. The results show that the random forest is significantly better than other machine learning algorithms on Tibetan text classification.
Open Access Status
This publication is not available as open access
First Page
145
Last Page
150
Funding Number
MCM20170206
Funding Sponsor
National Key Research and Development Program of China