AbstractPrevious literature identifies that students’ real-time feedback is important in the learning process. There are numerous studies that have collected students’ feedback in real time. However, they include several limitations of which the most important is analysing the feedback. In this thesis, we address these limitations by proposing a system that will automatically analyse students’ feedback in real time and present the analysis results to the lecturer. To create such a system, we propose the use of sentiment analysis.
The extensive literature highlights the importance of this research in the sentiment analysis field, as there is no research exploring sentiment analysis for students’ real-time feedback and research in the educational domain has been focused mainly on e-learning. The literature shows that emotions are also important in learning and could be detected using sentiment analysis. Consequently, this research is also important as there is little research in detecting emotion from students’ feedback.
Polarity detection is explored and two experiments were done to identify the best combination of preprocessing, features and machine learning techniques to create an optimal model for polarity detection of students’ feedback. As a result, we found that the optimal model was to use a lower level of preprocessing, unigrams for features and Complement Naive Bayes for the classifier.
Emotion detection in students’ feedback is also explored. Two experiments were done to find the optimal model to detect emotions related to learning from students’ feedback. The results showed that models which detect a single emotion have a better performance than multiple emotion models. Three emotions (i.e. Amused, Bored, and Excited) were more easily detected than others. The optimal model to detect emotion included a low level preprocessing, unigrams as features and Complement Naive Bayes as the classifier.
Sarcasm detection in students’ feedback is explored. Our results showed that the lower level of preprocessing led to the best performance. Moreover, by adding other features, such as polarity and emotions, to the unigrams, sarcasm detection increased. Lastly, the best classifier to detect sarcasm was Complement Naive Bayes.
|Date of Award||Feb 2016|
|Supervisor||Ella Haig (Supervisor), Sanaz Fallahkhair (Supervisor) & Alexander Gegov (Supervisor)|