This study presents a model for the early identification of students who are likely to fail in an academic course. To enhance predictive accuracy, sentiment analysis is used to identify affective information from text-based self-evaluated comments written by students. Experimental results demonstrated that adding extracted sentiment information from student self-evaluations yields a significant improvement in early-stage prediction quality. The results also indicate the limited early-stage predictive value of structured data, such as homework completion, attendance, and exam grades, due to data sparseness at the beginning of the course. Thus, applying sentiment analysis to unstructured data (e.g., self-evaluation comments) can play an important role in improving the accuracy of early-stage predictions. The findings present educators with an opportunity to provide students with real-time feedback and support to help students become self-regulated learners. Using the exploring results for improvement in teaching and learning initiatives is important to maintain students' performances and the effectiveness of the learning process.