  Subtext is a kind of deep semantics which can be acquired after one or more
rounds of expression transformation. As a popular way of expressing one's
intentions, it is well worth studying. In this paper, we try to make computers
understand whether there is a subtext by means of machine learning. We build a
Chinese dataset whose source data comes from the popular social media (e.g.
Weibo, Netease Music, Zhihu, and Bilibili). In addition, we also build a
baseline model called SASICM to deal with subtext recognition. The F1 score of
SASICMg, whose pretrained model is GloVe, is as high as 64.37%, which is 3.97%
higher than that of BERT based model, 12.7% higher than that of traditional
methods on average, including support vector machine, logistic regression
classifier, maximum entropy classifier, naive bayes classifier and decision
tree and 2.39% higher than that of the state-of-the-art, including MARIN and
BTM. The F1 score of SASICMBERT, whose pretrained model is BERT, is 65.12%,
which is 0.75% higher than that of SASICMg. The accuracy rates of SASICMg and
SASICMBERT are 71.16% and 70.76%, respectively, which can compete with those of
other methods which are mentioned before.
