The increasing amount of available subjective text data in internet such as product reviews, movie critiques and social media comments provides golden opportunities for information retrieval researchers to extract useful information out of such datasets. Topic modeling and sentiment analysis are two widely researched fields that separately try to find the different topics and sentiments in text data. The popular Latent Dirichlet Allocation (LDA) of topic modeling field has long been used for hidden topics extraction. There has been some recent advances in simultaneous topic and sentiment extraction that try to extend the topic modeling framework to be able to include sentiment extraction in the process. Each of the developed models seeks to retrieve specific levels of sentiments and topics. In this work we extend the LDA model to jointly extract topics and sentiments and estimate topic and topic-sentiment proportions in each document. This model can be used for categorizing documents of any objective text corpus into multiple groups based on estimated sentiment-topic categories and can be further used for a personalized topic-sentiment search. In a separate project on online education, we study Algebra Nation, an online tutoring platform to teach Algebra to K-12 students. In the first stage of this study, we investigate the impact of the platform on student performance at the end-of-course exam in grade 7 to 9 using Hierarchical Linear Models. Next, we study the implementation of the platform in three consecutive years by using the recorded usage data of students and their teachers and investigate the indirect effect of teachers on student final scores through teachers' encouragements to be more active students in the platform. In the final stage of the project, we take advantage of newly developed methods to find causal variables for final score prediction. Prediction of final scores can be used by the developers to make recommendations to students and by teachers to provide students with more targeted teaching experience, resulting in a more personalized learning experience. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]