Aspect-Based Opinion Mining on Student’s Feedback for Faculty Teaching Performance Evaluation

Abstract

Students' feedback is crucial for academic institutions in order to evaluate faculty performance. Handling the qualitative opinions of students efficiently while automatic report generation is a challenging task. Indeed, most organizations deal with quantitative feedback effectively, whereas qualitative feedback is either processed manually or ignored altogether. This study proposes a supervised aspect based opinion mining system based on two-layered LSTM model. The first layer predicts the aspects described within the feedback and later specifies the orientation (positive, negative, and neutral) of those predicted aspects. The model was tested on a manually tagged data set constructed from the last five years students' comments from Sukkur IBA University as well as on a standard SemEval-2014 data set. Unlike many other LSTM models proposed for other domains, the proposed model is quite simple in terms of architecture which results in less complexity. The system attains a good accuracy using the domain embedding layer in both tasks: aspect extraction (91%) and sentiment polarity detection (93%). To the best of our knowledge, this study is a first attempt that uses deep learning approach for performing aspect based sentiment analysis on students' feedback for evaluating faculty teaching performance.

Weakly Supervised Framework for Aspect-Based Sentiment Analysis on Students’ Reviews of MOOCs

“…It can be performed at a document level [10], [11], sentence level [12], [13], topic level and aspect (feature) level [14], [15]. It can further be categorized based upon the techniques used, such as, lexicon-based [16]- [18], featuresbased [10], [19]- [21], those using conventional machine learning approaches, i.e., Naive Bayes (NB), SVM [18], and unsupervised methods [14], and more recently deep learningbased sentiment analysis [12], [22]. A detailed description of techniques and approaches used to perform sentiment analysis is explored in the survey conducted in [23].…”

“…The closest research we could find as of today is presented by Sindhu et al in [21]. They proposed a two-layered LSTM model for aspect-based opinion mining of students' feedback.…”

“…We compared our approach to the one presented by Sindhu et al in [21] on their dataset. We specifically selected this dataset as a benchmark and their proposed approach to compare with ours, as this is the only manually labeled dataset available as of today.…”

“…An automatically annotated dataset is used to train the deep network model for the aspect sentiment classification as presented in paper [21]. The architecture of the network is relatively simple, comprising an embedding layer, dropout layer followed by a 196-unit LSTM, and the output layer depicting a probability distribution over three sentiment orientations, i.e., positive, neutral, and negative.…”

Mining Review Unit Model for Online Review Analysis

“…The JMTS model breaks from the sentence-level modeling assumption, but it combines words from adjacent sentences, resulting in reduced modeling ability. Sindhu and colleagues applied aspect-based sentiment analysis techniques to the field of education [22] and proposed a two-layered LSTM model for student feedback on faculty teaching performance. The first layer predicted the aspects described within the feedback and later specified the orientation of those predicted aspects.…”