abstract: Due to vast resources brought by social media services, social data mining has received increasing attention in recent years. The availability of sheer amounts of user-generated data presents data scientists both opportunities and challenges. Opportunities are presented with additional data sources. The abundant link information in social networks could provide another rich source in deriving implicit information for social data mining. However, the vast majority of existing studies overwhelmingly focus on positive links between users while negative links are also prevailing in real- world social networks such as distrust relations in Epinions and foe links in Slashdot. Though recent studies show that negative links have some added value over positive links, it is dicult to directly employ them because of its distinct characteristics from positive interactions. Another challenge is that label information is rather limited in social media as the labeling process requires human attention and may be very expensive. Hence, alternative criteria are needed to guide the learning process for many tasks such as feature selection and sentiment analysis. To address above-mentioned issues, I study two novel problems for signed social networks mining, (1) unsupervised feature selection in signed social networks; and (2) unsupervised sentiment analysis with signed social networks. To tackle the first problem, I propose a novel unsupervised feature selection framework SignedFS. In particular, I model positive and negative links simultaneously for user preference learning, and then embed the user preference learning into feature selection. To study the second problem, I incorporate explicit sentiment signals in textual terms and implicit sentiment signals from signed social networks into a coherent model Signed- Senti. Empirical experiments on real-world datasets corroborate the effectiveness of these two frameworks on the tasks of feature selection and sentiment analysis. / Dissertation/Thesis / Masters Thesis Computer Science 2017