Topic Discovery of Online Course Reviews Using LDA with Leveraging Reviews Helpfulness

Lubis, Fetty Fitriyanti; Rosmansyah, Yusep; Supangkat, Suhono Harso

doi:10.11591/ijece.v9i1.pp426-438

Cited by 12 publications

(12 citation statements)

References 16 publications

(26 reference statements)

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“…Table 8 shows papers that reported the sources of the datasets used for conducting experiments along with their corresponding categories and description. Here, the data were mostly collected by conducting surveys among students and teachers or by providing questioners to collect feedback from the students Education/research platforms [14,31,36,40,[44][45][46]48,58,61,70,78,82,84,86,93,95,99,101] This category contains the data extracted from online platforms providing different courses such as Coursera, edX, and research websites such as ResearchGate, LinkedIn, etc.…”

Section: Rq5 What Are the Most Common Sources Used To Collect Students' Feedback?mentioning

confidence: 99%

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

et al. 2021

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In the last decade, sentiment analysis has been widely applied in many domains, including business, social networks and education. Particularly in the education domain, where dealing with and processing students’ opinions is a complicated task due to the nature of the language used by students and the large volume of information, the application of sentiment analysis is growing yet remains challenging. Several literature reviews reveal the state of the application of sentiment analysis in this domain from different perspectives and contexts. However, the body of literature is lacking a review that systematically classifies the research and results of the application of natural language processing (NLP), deep learning (DL), and machine learning (ML) solutions for sentiment analysis in the education domain. In this article, we present the results of a systematic mapping study to structure the published information available. We used a stepwise PRISMA framework to guide the search process and searched for studies conducted between 2015 and 2020 in the electronic research databases of the scientific literature. We identified 92 relevant studies out of 612 that were initially found on the sentiment analysis of students’ feedback in learning platform environments. The mapping results showed that, despite the identified challenges, the field is rapidly growing, especially regarding the application of DL, which is the most recent trend. We identified various aspects that need to be considered in order to contribute to the maturity of research and development in the field. Among these aspects, we highlighted the need of having structured datasets, standardized solutions and increased focus on emotional expression and detection.

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Section: Rq5 What Are the Most Common Sources Used To Collect Students' Feedback?mentioning

confidence: 99%

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

et al. 2021

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“…Thus, the authors suggested using some mechanism for sampling method to modify imbalanced data into balanced distribution [15]. In 2019, authors of [16] performed sentiment classification with Lexicon Bing for subjectivity classification and LDA for topic modelling. However, the approach only used a lexicon-based approach for subjectivity classification, which might not be reliable for subjectivity detection as the biggest downside of the rule-based method is that the procedure only cares about individual words and lacks the context in which it is used [16].…”

Section: Sentiment Analysis With Subjectivity Classificationmentioning

confidence: 99%

Sentiment Attribution Analysis With Hierarchical Classification and Automatic Aspect Categorization on Online User Reviews

Win

Ravana²,

Shuib³

2022

MJCS

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Due to COVID-19 pandemic, most physical business transactions were pushed online. Online reviews became an excellent source for sentiment analysis to determine a customer's sentiment about a business. This insight is valuable asset for businesses, especially for tourism sector, to be harnessed for business intelligence and craft new marketing strategies. However, traditional sentiment analysis with flat classification and manual aspect categorization technique imposes challenges with non-opinionated reviews and outdated pre-defined aspect categories which limits businesses to filter relevant opinionated reviews and learn new aspects from reviews itself for aspect-based sentiment analysis. Therefore, this paper proposes sentiment attribution analysis with hierarchical classification and automatic aspect categorization to improve the social listening for diligent marketing and recommend potential business optimization to revive the business from surviving to thriving after this pandemic. Hierarchical classification is proposed using hybrid approach. While automatic aspect categorization is constructed with semantic similarity clustering and applied enhanced topic modelling on opinionated reviews. Experimental results on two real-world datasets from two different industries, Airline and Hotel, shows that the sentiment analysis with hierarchical classification outperforms the classification accuracy with a good F1-score compared to baseline papers. Automatic aspect categorization was found to be able to unhide the sentiment of the aspects which was not recognized in manual aspect categorization. Although it is accepted that the effectiveness of aspect-based sentiment analysis on flat classification and manual aspect categorization, none have assessed the effectiveness while using hierarchical classification with a hybrid approach and automatic aspect categorization.

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“…In addition to manual coding methodologies and questionnairebased surveys, a mainstream of automatic text mining approaches for MOOC learner satisfaction research is topic models [4,5,[13][14][15]), which, with the basis of term co-occurrences, have topics (i.e., collections of terms with high co-occurrence probabilities) as outputs [16,17]. A topic modeling analysis of 50 MOOCs [5] indicates that "learners focus more on course-related content with positive sentiment and course logistics and video production with negative sentiment (p. 670). "…”

Section: Literature Reviewmentioning

confidence: 99%

“…Such subjective biases lead to difficulties in result replication, and the limited sample sizes cause results to be less comprehensive. As such, computer-based text mining approaches like probabilistic topic models for textual course review analysis in MOOC learner (dis) satisfaction research are becoming increasingly important (e.g., [4,5]). However, the traditional way of applying topic models focuses primarily on negative reviews, leading to the potential danger of mixing the positive sides since reviewers usually do not complain from the beginning to the end in writing a negative review.…”

Section: Introductionmentioning

confidence: 99%

Understanding MOOC Reviews: Text Mining using Structural Topic Model

Chen¹,

Cheng²,

Xie³

et al. 2021

HCIS

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Understanding the reasons for MOOC learners' complaints is essential for MOOC providers to facilitate service quality and promote learner satisfaction. The current research uses structural topic modeling to analyze 21,692 programming MOOC course reviews in Class Central, leading to enhanced inference on learner (dis)satisfaction. Four topics appear more commonly in negative reviews as compared to positive ones. Additionally, variations in learner complaints across MOOC course grades are explored, indicating that learners' main complaints about high-graded MOOCs include problem-solving, practices, and programming textbooks, whereas learners of low-graded courses are frequently annoyed by grading and course quality problems. Our study contributes to the MOOC literature by facilitating a better understanding of MOOC learner (dis)satisfaction using rigorous statistical techniques. Although this study uses programming MOOCs as a case study, the analytical methodologies are independent and adapt to MOOC reviews of varied subjects.

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Topic Discovery of Online Course Reviews Using LDA with Leveraging Reviews Helpfulness

Cited by 12 publications

References 16 publications

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

Sentiment Attribution Analysis With Hierarchical Classification and Automatic Aspect Categorization on Online User Reviews

Understanding MOOC Reviews: Text Mining using Structural Topic Model

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