A Sentiment-Based Approach to Predict Learners’ Perceptions Towards YouTube Educational Videos

Faizi, Rdouan

doi:10.1007/978-3-030-96299-9_52

Cited by 5 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors found that their proposed hybrid classifier performed better than the standalone SVM classifier. Dolianiti et al [49] performed document and sentence level sentiment annotation by using IBM Watson Natural Language Understanding, Microsoft Azure Text Analytics API, OpinionFinder 2.0, Repustate, and Senti-strength in educational domain text reviews data. The authors used two student's reviews data pertaining to the learning management system.…”

Section: Machine Learningmentioning

confidence: 99%

ORDSAENet: Outlier Resilient Semantic Featured Deep Driven Sentiment Analysis Model for Education Domain

B A,

K N

2023

JMC

View full text Add to dashboard Cite

The high pace rising global competitions across education sector has forced institutions to enhance aforesaid aspects, which require assessing students or related stakeholders’ perception and opinion towards the learning materials, courses, learning methods or pedagogies, etc. To achieve it, the use of reviews by students can of paramount significance; yet, annotating student’s opinion over huge heterogenous and unstructured data remains a tedious task. Though, the artificial intelligence (AI) and natural language processing (NLP) techniques can play decisive role; yet the conventional unsupervised lexicon, corpus-based solutions, and machine learning and/or deep driven approaches are found limited due to the different issues like class-imbalance, lack of contextual details, lack of long-term dependency, convergence, local minima etc. The aforesaid challenges can be severe over large inputs in Big Data ecosystems. In this reference, this paper proposed an outlier resilient semantic featuring deep driven sentiment analysis model (ORDSAENet) for educational domain sentiment annotations. To address data heterogeneity and unstructured-ness over unpredictable digital media, the ORDSAENet applies varied pre-processing methods including missing value removal, Unicode normalization, Emoji and Website link removal, removal of the words with numeric values, punctuations removal, lower case conversion, stop-word removal, lemmatization, and tokenization. Moreover, it applies a text size-constrained criteria to remove outlier texts from the input and hence improve ROI-specific learning for accurate annotation. The tokenized data was processed for Word2Vec assisted continuous bag-of-words (CBOW) semantic embedding followed by synthetic minority over-sampling with edited nearest neighbor (SMOTE-ENN) resampling. The resampled embedding matrix was then processed for Bi-LSTM feature extraction and learning that retains both local as well as contextual features to achieve efficient learning and classification. Executing ORDSAENet model over educational review dataset encompassing both qualitative reviews as well as quantitative ratings for the online courses, revealed that the proposed approach achieves average sentiment annotation accuracy, precision, recall, and F-Measure of 95.87%, 95.26%, 95.06% and 95.15%, respectively, which is higher than the LSTM driven standalone feature learning solutions and other state-of-arts. The overall simulation results and allied inferences confirm robustness of the ORDSAENet model towards real-time educational sentiment annotation solution.

show abstract

Section: Machine Learningmentioning

confidence: 99%

ORDSAENet: Outlier Resilient Semantic Featured Deep Driven Sentiment Analysis Model for Education Domain

B A,

K N

2023

JMC

View full text Add to dashboard Cite

show abstract

“…Sentiment analysis applications have already been implemented in a variety of sectors. Nevertheless, one of the domains in which these systems has recently been gaining ground is education [9,10,11,12,13].…”

Section: Introductionmentioning

confidence: 99%

Using Sentiment Analysis to Explore Student Feedback: A Lexical Approach

Faizi

2023

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

Given the increasing abundance of online courses over the last couple of years, new forms of student feedback, which are less frequently used by teachers, have been generated in massive amounts. Nonetheless, extracting and processing this student generated content manually is costly and time consuming. In this respect, our objective in this paper is to propose a lexical-based approach that can predict the underlying sentiments of each student review, thus, enabling teachers to assess to what extent are students satisfied with the online learning resources and teaching practices. To enhance the performance of the proposed approach, a new education sentiment lexicon was built and incorporated into the model. After its implementation on a dataset that was extracted from the Web, this sentiment analysis lexical approach has proven to correctly predict the sentiment polarities of the great majority (i.e. 86.45%) of student feedback.

show abstract

“…The textual comments were manually labelled as positive or negative as their sentiment. A naïve Bayes classifier was used to train the labelled dataset to classify the comments into positive or negative.Faizi[54] proposed a machine learning approach to classify learners' feedback sentiment towards Youtube educational videos. Traditional machine learn-ing methods such as random forest, logistic regression, Naïve Bayes, and SVM were adopted to learn the feedback datasets and classify the learner comments with sentiment as positive or negative.…”

mentioning

confidence: 99%

Sentiment analysis and opinion mining on educational data: A survey

Shaik,

Tao,

Dann

et al. 2023

Preprint

View full text Add to dashboard Cite

Sentiment analysis AKA opinion mining is one of the most widely used NLP applications to identify human intentions from their reviews. In the education sector, opinion mining is used to listen to student opinions and enhance their learning-teaching practices pedagogically. With advancements in sentiment annotation techniques and AI methodologies, student comments can be labelled with their sentiment orientation without much human intervention. In this review article, 1) we consider the role of emotional analysis in education from four levels: document level, sentence level, entity level, and aspect level, 2)sentiment annotation techniques including lexicon-based and corpus-based approaches for unsupervised annotations are explored, 3)the role of AI in sentiment analysis with methodologies like machine learning,

show abstract

A Sentiment-Based Approach to Predict Learners’ Perceptions Towards YouTube Educational Videos

Cited by 5 publications

References 15 publications

ORDSAENet: Outlier Resilient Semantic Featured Deep Driven Sentiment Analysis Model for Education Domain

ORDSAENet: Outlier Resilient Semantic Featured Deep Driven Sentiment Analysis Model for Education Domain

Using Sentiment Analysis to Explore Student Feedback: A Lexical Approach

Sentiment analysis and opinion mining on educational data: A survey

Contact Info

Product

Resources

About