Sentiment Analysis for YouTube Educational Videos Using Machine and Deep Learning Approaches

Alhujaili, Rawan Fahad; Yafooz, Wael M. S.

doi:10.1109/icetci55101.2022.9832284

Cited by 8 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SVC and RF classifiers gave an accuracy of 96% while 74% was the worst accuracy given by the KNN classifier using the under-sampling technique. DL model for oversampling and SOMTE technique gave 96% and 89% accuracy was obtained for manually balanced data set [18].…”

Section: Related Workmentioning

confidence: 93%

“…Oversampling by SMOTE is quite famous in literature but SMOTE technique can be applied after the vectorization of the data. As this research required the same data for all three techniques, oversampling was done with repetition as [18] suggests that both SMOTE and oversampling techniques evaluated similar results. The annotated data had 1175 positive comments 165 negative and 660 neutral comments.…”

Section: Minority Oversamplingmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Approach to Measure Students’ Satisfaction on YouTube Educational Videos

Naz¹,

Hina²,

Fatima

et al. 2023

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

Every aspect of life has undergone innovation in recent years. Information Technology has revolutionized the concept of teaching and learning. Both educators and students are now utilizing contemporary resources for learning and teaching. One of the primary sources of contemporary educational resources is educational videos posted on various channels on YouTube. This study intends to investigate the opinions voiced in YouTube comments for educational videos with the help of Sentiment Analysis. In this research, three techniques namely SentiStrength, TextBlob, and Naive Bayes Classifier were utilized to analyze the sentiments. According to the results, the majority of the remarks made about the videos have positive sentiments that concluded that students are satisfied with YouTube educational videos and consider YouTube as a beneficial learning tool.

show abstract

Section: Related Workmentioning

confidence: 93%

Section: Minority Oversamplingmentioning

confidence: 99%

A Hybrid Approach to Measure Students’ Satisfaction on YouTube Educational Videos

Naz¹,

Hina²,

Fatima

et al. 2023

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

show abstract

“…It www.ijacsa.thesai.org offers an integrated solution for the challenges of preprocessing Arabic text on social media. This was undertaken to investigate the performance metrics as given in [23,24,25,26,27,28,29] and validates the proposed model for small-and large-scale datasets. Disambiguation using the deep learning techniques with the Arabic corpus is presented in [30].…”

Section: Related Studiesmentioning

confidence: 99%

Utilizing Deep Learning in Arabic Text Classification Sentiment Analysis of Twitter

Ibrahim¹,

Yafooz²,

Emara³

et al. 2022

IJACSA

View full text Add to dashboard Cite

The number of social media users has increased. These users share and reshare their ideas in posts and this information can be mined and used by decision-makers in different domains, who analyse and study user opinions on social media networks to improve the quality of products or study specific phenomena. During the COVID-19 pandemic, social media was used to make decisions to limit the spread of the disease using sentiment analysis. Substantial research on this topic has been done; however, there are limited Arabic textual resources on social media. This has resulted in fewer quality sentiment analyses on Arabic texts. This study proposes a model for Arabic sentiment analysis using a Twitter dataset and deep learning models with Arabic word embedding. It uses the supervised deep learning algorithms on the proposed dataset. The dataset contains 51,000 tweets, of which 8,820 are classified as positive, 37,360 neutral, and 8,820 as negative. After cleaning it will contain 31,413. The experiment has been carried out by applying the deep learning models, Convolutional Neural Network and Long Short-Term Memory while comparing the results of different machine learning techniques such as Naive Bayes and Support Vector Machine. The accuracy of the AraBERT model is 0.92% when applying the test on 3,505 tweets.

show abstract

“…In the third phase, the classification process is carried out by using the most common MLCs and DL approaches used scholarly [28,29]. In MCLs, the most common classifiers used are the Random Forest, Gradient Boosting Classifier, Neural Net,poly SVM, Decision Tree, AdaBoost, Nearest Neighbors, Stochastic Gradient Descent (SGD), Naive Bayes, and QDA.…”

Section: Classification Processmentioning

confidence: 99%

“…This section presents and demonstrates the results of the experiments of most popular classifiers used in our model [28,29]. The experiments evaluated in this section used a confusion matrix to show the detection performance of the models.…”

Section: A Machine Learning Experimentsmentioning

confidence: 99%

Ransomware Detection using Machine and Deep Learning Approaches

Alsaidi¹,

Yafooz²,

Alolofi³

et al. 2022

IJACSA

View full text Add to dashboard Cite

Due to the advancement and easy accessibility to computer and internet technology, network security has become vulnerable to hacker threats. Ransomware is a frequently used malware in cyber-attacks to trick the victim users to expose sensitive and private information to the attackers. Consequently, victims may not be able to access their data any longer until they pay a ransom for stolen files or data. Different methods have been introduced to overcome these issues. It is evident through an extensive literature review that some lexical features are not always sufficient to detect categories of malicious URLs. This paper proposes a model to detect Ransomware using machine and deep learning approaches. This model was introduced as a novel feature for classification using the idea that starts with "https://www." This feature was not considered in the earlier papers on malicious URLs identification. In addition, this paper introduced a novel dataset that consists of 405,836 records. Two main experiments were carried out utilizing malicious URL features to defend Ransomware using the proposed dataset. Moreover, to enhance and optimize the experimental accuracy, various hyper-parameters were tested on the same dataset to define the optimal factors of every method. According to the comparative and experimental results of the applied classification techniques, the proposed model achieved the best performance at 99.8% accuracy rate for detecting malicious URLs using machine and deep learning.

show abstract

Sentiment Analysis for YouTube Educational Videos Using Machine and Deep Learning Approaches

Cited by 8 publications

References 18 publications

A Hybrid Approach to Measure Students’ Satisfaction on YouTube Educational Videos

A Hybrid Approach to Measure Students’ Satisfaction on YouTube Educational Videos

Utilizing Deep Learning in Arabic Text Classification Sentiment Analysis of Twitter

Ransomware Detection using Machine and Deep Learning Approaches

Contact Info

Product

Resources

About