Sentiment Analysis of Student Textual Feedback to Improve Teaching

Mabunda, Judith Goodness Khanyisa; Jadhav, Ashwini; Ajoodha, Ritesh

doi:10.1201/9781003202240-100

Cited by 9 publications

(10 citation statements)

References 5 publications

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“…From our ndings as illustrated in Table 5, we found out that 76% of the papers that were reviewed were based on extracting students' thoughts, opinion and attitudes toward teachers and 16% were based on extracting students' opinion towards courses and institution, whereas the remaining 8% were based on extraction student opinion towards institution. [9], [10], [11], [12], [13], [2], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29].…”

Section: Resultsmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

“…SVM [2], [7], [8], [9], [12], [15], [16], [17], [19], [20], [21], [24], [25], [38], [41], [48], [51] [52],…”

Section: Supervised Learning Modelsunclassified

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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].…”

Section: Learning Techniques Papersunclassified

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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“…Sentiment analysis is the most important task of NLP, in which it analyses the community's opinions about social actions such as social media apps, academic activities, and technology [1,2]. Sentiment analysis is the analysis of opinions about users [3,4].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Approach Used to Analyze the Sentiments of Romanized Text (Sindhi)

Sodhar¹,

Sulaiman²,

Buller³

et al. 2023

IJACSA

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Sentiment analysis is an important part of natural language processing (NLP). This study evaluated the sentiment of Romanized Sindhi Text (RST) using a hybrid approach and ground truth values. The methodology of sentiment analysis involves three major steps: input data, process on tool, analysis of data and evaluation of results. One hundred RST sentences were used in this study's sentiment analysis, which can be positive, neutral, or negative. The statements in the corpus of this study are simple to understand and are used in everyday life. This research used an online Python tool to process a text and get results in the form of outcomes. The results showed that 86% of the sentences have neutral sentiments, 9% of the total results of sentiment analysis have negative sentiments, and only 5% of sentences of Romanized Sindhi Text have positive sentiments. The accuracy of the RST was measured on an online calculator and the value was 87.02% on the basis of ground truth values. An error ratio of 12.98% was calculated on the basis accuracy found on the online calculator of confusion matrix.

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Sentiment Analysis of Student Textual Feedback to Improve Teaching

Cited by 9 publications

References 5 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)

Hybrid Approach Used to Analyze the Sentiments of Romanized Text (Sindhi)

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