Text classification based on machine learning for Tibetan social network

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.