Triggers and Tweets: Implicit Aspect-Based Sentiment and Emotion Analysis of Community Chatter Relevant to Education Post-COVID-19

Ismail, Heba; Khalil, Ashraf; Hussein, Nada; Elabyad, Rawan

doi:10.3390/bdcc6030099

Cited by 10 publications

(5 citation statements)

References 46 publications

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“…The aim of these studies was to assist academic institutions in identifying and addressing student issues through feedback analysis. The data for these studies was primarily gathered from social media platforms like Twitter and Facebook, as mentioned in [20], [21], [22], [23]. Other studies utilized data collected from the institution, such as MOOC platforms or traditional institution surveys, as seen in [24], [25], [26].…”

Section: A Absa In Educational Domainmentioning

confidence: 99%

BERT-Based Model for Aspect-Based Sentiment Analysis for Analyzing Arabic Open-Ended Survey Responses: A Case Study

Alshaikh,

Almatrafi,

Abushark

2024

IEEE Access

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Educational institutions typically gather feedback from beneficiaries through formal surveys.Offering open-ended questions allows students to express their opinions about matters that may not have been measured directly in closed-ended questions. However, responses to open-ended questions are typically overlooked due to the time and effort required. Aspect-based sentiment analysis is used to automate the process of extracting fine-grained information from texts. This study aims to 1) examine the performance of different BERT-based models for aspect term extraction for Arabic text sourced from educational institution surveys; 2) develop a system that automates the ABSA process in a way that will automatically label survey responses. An end-to-end system was developed as a case study to extract aspect terms, identify their polarity, map extracted aspects to their respective categories, and aggregate category polarity. To accomplish this, the models were evaluated using an in-house dataset. The result showed that FAST-LCF-ATEPC, a multilingual checkpoint, outperformed other models including AraBERT, MARBERT, and QARiB, in the aspect-term extraction task, with an F1 score of 0.58. Hence, it was used for aspect-term polarity classification, showing an F1 score of 0.86. Mapping aspects to their respective categories using a predefined list yielded an average F1 score of 0.98. Furthermore, the polarities of the categories were aggregated to summarize the overall polarity for each category. The developed system can support Arabic educational institutions in harnessing valuable information in responses to open-ended survey questions, allowing decision-makers to better allocate resources, and improve facilities, services, and students' learning experiences.

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Section: A Absa In Educational Domainmentioning

confidence: 99%

BERT-Based Model for Aspect-Based Sentiment Analysis for Analyzing Arabic Open-Ended Survey Responses: A Case Study

Alshaikh,

Almatrafi,

Abushark

2024

IEEE Access

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“…The results concerning personal experiences are of course intimately linked to students' perceptions about the quality of the online education they received. Indeed, other works makes a direct link between low motivation and the feeling that online education is not as good as that delivered in traditional face-to-face settings (e.g., [79,80]). The forced nature of the COVID-19 online learning experience is also a factor cited by other authors as leading to the lack of enjoyment reported with respect to online courses (e.g., [81,82]).…”

Section: Student Adaptation To Online Educationmentioning

confidence: 99%

Resilient Strategies for Internet-Based Education: Investigating Engineering Students in the Canary Islands in the Aftermath of COVID-19

Sierra,

Boente,

Zitouni

et al. 2024

Sustainability

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The COVID-19 pandemic has brought about notable changes in the education sector, specifically the shift towards online learning. This study examined the experiences of 124 engineering students in the Canary Islands, an EU ultra-peripheral region, as they adapted to online education during the pandemic. A comprehensive survey assessed students' experiences in five key dimensions, including satisfaction with traditional face-to-face learning, perceptions of the engineering department's transition to online learning, module-specific adaptations, personal adaptation strategies, and the adaptation of teaching staff. The study's methodology involved statistical analyses using Microsoft Excel v16.0 and SPSS 27 tools to identify patterns and draw conclusions. The findings indicate a nuanced landscape. Students demonstrated strong technological literacy and readiness for online learning. However, they expressed concerns about educators' digital proficiency and perceived a decline in educational quality. These results emphasize the critical need for sustainable, adaptable, and inclusive educational strategies, particularly in regions like the Canary Islands that face unique challenges. The implications of the study have broader relevance to digital education. It is essential to note the need for educators to receive comprehensive training in digital tools and methodologies to improve the quality of online learning.

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“…Once the data is preprocessed, it is subjected to sentiment computation. This component involves using machine learning algorithms to classify the text into different sentiment categories: positive, neutral, or negative [10].…”

Section: Sentiment Computation Based On Educational Psychologymentioning

confidence: 99%

Sentiment analysis in online education: An analytical approach and application

Lin

2024

ACE

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This paper presents a groundbreaking approach to the application of sentiment analysis within the domain of online education. By introducing an innovative methodology, the aim is to streamline the process of automatically evaluating sentiments and extracting opinions from the vast sea of content produced by learners during their online interactions. This not only aids educators in swiftly gauging the general mood and perspective of their student body, but also allows them to delve deeper into the nuanced feedback provided, thus ensuring the continual improvement of course quality. In an era where digital learning platforms are growing exponentially, understanding students' attitudes, concerns, and overall satisfaction is paramount. Our methodology, therefore, is not just a technical advancement, but also a strategic tool for educational institutions aiming to thrive in the digital age. The current research landscape, while expansive, has often overlooked the significance of real-time sentiment analysis in e-learning environments. This study, therefore, bridges an important gap, bringing to the forefront the importance of harnessing student feedback in a digital format, allowing educators to tailor their approach for optimal student engagement and success.

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Triggers and Tweets: Implicit Aspect-Based Sentiment and Emotion Analysis of Community Chatter Relevant to Education Post-COVID-19

Cited by 10 publications

References 46 publications

BERT-Based Model for Aspect-Based Sentiment Analysis for Analyzing Arabic Open-Ended Survey Responses: A Case Study

BERT-Based Model for Aspect-Based Sentiment Analysis for Analyzing Arabic Open-Ended Survey Responses: A Case Study

Resilient Strategies for Internet-Based Education: Investigating Engineering Students in the Canary Islands in the Aftermath of COVID-19

Sentiment analysis in online education: An analytical approach and application

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