Data Mining Practical Machine Learning Tools and Techniques

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

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Sentiment Analysis and Opinion Mining

Discourse in the Novel

A grammar of spoken Chinese = 中國話的文法

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Disciplinary Discourses: Social Interactions in Academic Writing

Sentiment analysis is one of the great accomplishments of the last decade in the field of Language Technologies. In this paper, we explore mining collective sentiment from forum posts in a Massive Open Online Course (MOOC) in order to monitor students’ trending opinions towards the course and major course tools, such as lecture and peer-assessment. We observe a correlation between sentiment ratio measured based on daily forum posts and number of students who drop out each day. On a user-level, we evaluate the impact of sentiment on attrition over time. A qualitative analysis clarifies the subtle differences in how these language behaviors are used in practice across three MOOCs. Implications for research and practice are discussed.

https://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/130_EDM-2014-Full.pdf

Sentiment Analysis in MOOC Discussion Forums: What does it tell us?

In this paper, we explore student dropout behavior in a Massively Open Online Course (MOOC). We use a survival model to measure the impact of three social factors that make predictions about attrition along the way for students who have participated in the course discussion forum.

Social factors that contribute to attrition in MOOCs

Thousands of students enroll in Massive Open Online Courses~(MOOCs) to seek opportunities for learning and self-improvement. However, the learning process often involves struggles with confusion, which may have an adverse effect on the course participation experience, leading to dropout along the way. In this paper, we quantify that effect. We describe a classification model using discussion forum behavior and clickstream data to automatically identify posts that express confusion. We then apply survival analysis to quantify the impact of confusion on student dropout. The results demonstrate that the more confusion students express or are exposed to, the lower the probability of their retention. Receiving support and resolution of confusion helps mitigate this effect. We explore the differential effects of confusion expressed in different contexts and related to different aspects of courses. We conclude with implications for design of interventions towards improving the retention of students in MOOCs.

Exploring the Effect of Confusion in Discussion Forums of Massive Open Online Courses

While data from Massive Open Online Courses (MOOCs) offers the potential to gain new insights into the ways in which online communities can contribute to student learning, much of the richness of the data trace is still yet to be mined. In particular, very little work has attempted fine-grained content analyses of the student interactions in MOOCs. Survey research indicates the importance of student goals and intentions in keeping them involved in a MOOC over time.  Automated fine-grained content analyses offer the potential to detect and monitor evidence of student engagement and how it relates to other aspects of their behavior. Ultimately these indicators reflect their commitment to remaining in the course.  As a methodological contribution, in this paper we investigate using computational linguistic models to measure learner motivation and cognitive engagement from the text of forum posts.  We validate our techniques using survival models that evaluate the predictive validity of these variables in connection with attrition over time. We conduct this evaluation in three MOOCs focusing on very different types of learning materials. Prior work demonstrates that participation in the discussion forums at all is a strong indicator of student commitment. Our methodology allows us to differentiate better among these students, and to identify danger signs that a struggling student is in need of support within a population whose interaction with the course offers the opportunity for effective support to be administered.  Theoretical and practical implications will be discussed.

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Linguistic Reflections of Student Engagement in Massive Open Online Courses

While MOOCs undoubtedly provide valuable learning resources for students, little research in the MOOC context has sought to evaluate students’ learning gains in the environment. It has been long acknowledged that conversation is a significant way for students to construct knowledge and learn. However, rather than studying learning in MOOC discussion forums, the thrust of current research in that context has been to identify factors that predict dropout. Thus, cognitively relevant student behavior in the forums has not been evaluated for its impact on cognitive processes and learning. In this paper, we adopt a content analysis approach to analyze students’ cognitively relevant behaviors in a MOOC discussion forum and further explore the relationship between the quantity and quality of that participation with their learning gains. As an integral part of our approach, we built a computational model to automate the analysis so that it is possible to extend the content analysis to all communication that occurred in the MOOC. We identified significant associations between discourse behavior and learning. Theoretical and practical implications are discussed.

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Miaomiao Wen

Papers

Sentiment Analysis in MOOC Discussion Forums: What does it tell us?

Social factors that contribute to attrition in MOOCs

Exploring the Effect of Confusion in Discussion Forums of Massive Open Online Courses

Linguistic Reflections of Student Engagement in Massive Open Online Courses

Investigating How Student's Cognitive Behavior in MOOC Discussion Forums Affect Learning Gains.