Sentence-Level Sentiment Analysis for Student Feedback Relevant to Teaching Process Assessment

Chantamuang, Orathai; Polpinij, Jantima; Vorakitphan, Vorakit; Luaphol, Bancha

doi:10.1007/978-3-031-20992-5_14

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Table 10: Dataset sources that have been used by the reviewed papers S/N Category of dataset Papers Description 1 Questionnaires/ Surveys [7], [10], [19], [22], [23], [29], [30], [31], [35], [40], [42], [43], [51], [52], [57], [58], [59], [61].…”

Section: Textblobmentioning

confidence: 99%

A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning

Oghu

Ogbuju

Abiodun

et al. 2023

Preprint

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The education industry considers quality to be a crucial factor in its development. Yet, the quality of many institutions is far from perfect as there is high rate of systemic failure and low performance among students. Consequently, the application of digital computing plays an increasingly important role in assuring the overall quality of an educational institution. However, literature lacks a reasonable number of systematic review that classifies researches that applied natural language processing and machine learning solutions for students’ feedback in sentiment analysis and quality assurance. Thus, this paper presents a systematic literature review that structure available published papers between 2014 and 2023 in high impact journal indexed databases. The work extracted 59 relevant papers out of the 3392 that was initially found using an exclusion and inclusion criteria. The result identified five (5) prevalent techniques that are majorly researched for sentiment analysis in the area of education as well as the prevalent supervised machine learning algorithms, lexicon-based approaches and evaluation metrics in assessing feedbacks in the education domain.

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Section: Textblobmentioning

confidence: 99%

A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning

Oghu

Ogbuju

Abiodun

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Supervised [7], [8], [9], [10], [11], [12], [13], [2], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29]. Unsupervised [30], [31], [32], [33], [34]. Lexicon-based [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47].…”

Section: Learning Techniques Papersmentioning

confidence: 99%

“…A tabular representation of these papers and dataset source is shown in Table 10. Questionnaires/ Surveys [7], [10], [19], [22], [23], [29], [30], [31], [35], [40], [42], [43], [51], [52], [57], [58], [59], [61].…”

Section: R E T R a C T E Dmentioning

confidence: 99%

A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning (RETRACTED)

Oghu,

Ogbuju,

Abiodun

et al. 2023

Bull.Socinf.The.App

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The education industry considers quality to be a crucial factor in its development. Nevertheless, the quality of many institutions is far from perfect, as there is a high rate of systemic failure and low performance among students. Consequently, the application of digital computing plays an increasingly important role in assuring the overall quality of an educational institution. However, the literature lacks a reasonable number of systematic reviews that classify research that applied natural language processing and machine learning solutions for students’ sentiment analysis and quality assurance feedback. Thus, this paper presents a systematic literature review that structure available published papers between 2014 and 2023 in a high-impact journal-indexed database. The work extracted 59 relevant papers from the 3392 initially found using exclusion and inclusion criteria. The result identified five (5) prevalent techniques that are majorly researched for sentiment analysis in education and the prevalent supervised machine learning algorithms, lexicon-based approaches, and evaluation metrics in assessing feedback in the education domain.

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Aspect based sentiment analysis on student feedback using machine learning techniques

Shah,

Nikam,

Zambre

et al. 2024

AIP Conference Proceedings

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Sentence-Level Sentiment Analysis for Student Feedback Relevant to Teaching Process Assessment

Cited by 5 publications

References 9 publications

A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning

A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning

A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning (RETRACTED)

Aspect based sentiment analysis on student feedback using machine learning techniques

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