  This work presents a supervised method for generating a classifier model of
the stances held by Chinese-speaking politicians and other Twitter users. Many
previous works of political tweets prediction exist on English tweets, but to
the best of our knowledge, this is the first work that builds prediction model
on Chinese political tweets. It firstly collects data by scraping tweets of
famous political figure and their related users. It secondly defines the
political spectrum in two groups: the group that shows approvals to the Chinese
Communist Party and the group that does not. Since there are not space between
words in Chinese to identify the independent words, it then completes
segmentation and vectorization by Jieba, a Chinese segmentation tool. Finally,
it trains the data collected from political tweets and produce a classification
model with high accuracy for understanding users' political stances from their
tweets on Twitter.
