Identification of Disengagement Factors using Sentiment Analysis and TF-IDF
This is the documentation repository for the master thesis: "MOOC Interrupted Determination of Disengagement Factors using Sentiment Analysis" at the Humboldt University of Berlin (Sept 2016).
Within the past few years, Massive Online Open Courses (MOOCs), have experienced a sudden rise in popularity, reaching thousands of students across the globe and bridging limitations that higher education has experienced until present. Despite the great potential MOOCs represent to the future of global education, a high drop-out phenomenon (96%) has been regularly reported over the years. This thesis investigates this phenomenon and uses Sentiment Analysis and other Text Mining techniques to identify topics related to the student drop-out. An en-to-end framework is presented including data extraction (50K textual reviews) and preparation, polarity classification and feature selection. Using the proposed framework, topics related to student dissatisfaction were found, supporting findings from previous studies. Furthermore, quantitative text metrics were used to identify linguistic patterns among dissatisfied students’ reviews, finding significant correlations with course features such as price, rating and polarity.
Following repository contains:
- The source code for the crawler written in order to extract the data (Py)//
- The implementation of the project (R)// 2.1. Descriptive Data Analysis// 2.2. Quantitative Text Analysis// 2.3. Sentiment Analysis / Polarity Classification// 2.4. Identification of salient terms using the TF-IDF weighting//
- Source code of the paper (LaTeX)//
MIT License
Copyright (c) [2016] [Laura Gabrysiak Gomez]
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