With the rise of computational social science

analyzing social sentiment with data mining methods has attracted widespread attention and has become a hot spot in recent years.Existing researches of sentiment analysis mainly focus on modern text

but hardly involve the ancient short text literature.This paper proposes a short text feature extension based transfer learning model CATL-PCO(Correlation Analysis Transfer Learning-Probability Co-occurrence).Through sentiments analysis in ancient literature

this paper can discovery social and cultural development in the ancient era.CATL-PCO expands the ancient literature feature vector based on the frequent word pairs

and utilizes transfer learning method to train three sentiment classifiers.CATL-PCO solves the problem of sparsity of short text feature vector

and the scarcity of modern translation

which improves the cognition of Chinese History.Experiments demonstrate the effectiveness of the proposed method on the dataset of Chinese poems in Tang Dynasty.Moreover

different periods of Tang and Song Dynasty

and different genres are analyzed in this paper in details.