Abstract
Automatically recognizing personality is a promising subject as a way to infer a person'sbehaviors. Many studies have been performed in recent years. However, very few of them are focus on predicting personality from Chinese texts. Chinese texts are very different from English texts where words are separated by the spaces. A Chinese sentence consists of a sequence of characters with no space between them. But a character is not a meaningful unit, a word is. This makes it more dicult to analyze Chinese texts since the boundaries of words are not obvious. In this thesis, we attempt to classify the personality traits from Chinese texts. We collected a dataset with posts and personality scores of the 222 Facebook users who use Chinese as their main written language. Then, the Jieba Chinese text segmentation was employed to accomplish the text segmentation task, and SVM was used as a learning algorithm for personality classi cation. Experimental results show that the performance in precision and recall gain much improvement with the help of text segmentation and considering both the text and friend features yields the best performance. Moreover, we nd that extraverts seem to write more sentences and use more common words than introverts do. This indicates that the extraverts are more willing to share their mood and life with others than the introverts.