Aspect-based sentiment analysis using topic modelling on student evaluations

Abstract

Aspect-Based Sentiment Analysis (ABSA) is a Natural Language Processing (NLP) task that focuses on identifying and extracting sentiment related to specific aspects or components of various subjects, including but not limited to products or services. In ABSA, the process typically involves several steps. First, aspects or features relevant to the product or service are identified from the text. These aspects could encompass specific attributes, functionalities, or components. Next, sentiment analysis is performed to determine the polarity (positive, negative, or neutral) associated with each aspect based on the context within the sentence or document. Finally, the results are aggregated to provide an overall sentiment for each aspect. This mini-dissertation investigates a proposed novel approach for aspect-based sentiment analysis using topic modelling on student evaluation data from the Department of Statistics provided by the University of Pretoria. Using ABSA in the higher educational field is significant since it provides insights on how students view certain aspects. These insights are useful for lecturers, as well as the Head of the Department or even the Dean, because they can make certain decisions based on the insights. The mini-dissertation utilises topic models for aspect extraction. Among these, the Latent Dirichlet Allocation (LDA) topic model is widely recognised. However, literature indicates that the LDA model performs better on longer texts, such as newspaper articles or e-books, rather than shorter texts like tweets. Since the student evaluations used in this research are short texts, the LDA model may not be the most suitable. Therefore, two alternative topic models, the Biterm Topic Model (BTM) and the Dirichlet Multinomial Mixture model (DMM), which are designed for short texts, are also applied to the data. These three topic models are applied in conjunction with an automatic text summarisation method for aspect extraction. As expected, the LDA topic model did not perform as well as the BTM an DMM models. Analysing the results from the BTM and DMM models, it was evident that the coherence scores from the BTM model were higher than the DMM, which indicates that the BTM model has a better ability to capture the underlying topics and relationships within the data compared to the DMM. After the topic modelling was applied, two sentiment analysis methods, the Multinomial Na¨ıve Bayes method, which is a machine learning technique, and the VADER method, which is a lexicon-based approach, were applied to the educational data. When these two methods were applied to the data it was found that the Multinomial Na¨ıve Bayes approach produced sentiments that were skewed to the negative side. On the other hand, the VADER method produced sentiments that were more evenly spread between positive, neutral and negative sentiments. Therefore, the VADER method was the preferred method. These findings underscore the importance of selecting an appropriate topic modelling approach and sentiment method for aspect-based sentiment analysis tasks. Key insights and recommendations from analysing the student evaluation data using the proposed new approach to aspect-based sentiment analysis highlight several improvements that the lecturers could consider. These include incorporating pre-recorded videos into the curriculum to accommodate various learning preferences, establishing a peer-review system to reduce errors in assignments and tests, and decreasing the number of pre-class and post-class tests for senior students to better manage their workload. Additionally, customising support and resources to address the specific needs of different student groups and enhancing communication channels between students and faculty to ensure student queries are effectively addressed are also recommended. These recommendations aim to improve the overall learning experience and meet the diverse needs of students.