Matches in SemOpenAlex for { <https://semopenalex.org/work/W4328011153> ?p ?o ?g. }
Showing items 1 to 76 of
76
with 100 items per page.
- W4328011153 abstract "The COVID-19 pandemic spread worldwide in the year 2020 and became a global health emergency. This pandemic has brought awareness that social distancing and quarantine are ideal ways to protect people in the community from infection. Therefore, Saudi Arabia used online learning instead of stopping it completely to continue the education process. This paper proposes to use machine-learning algorithms for Arabic sentiment analysis to find out what students and teaching staff thought about online learning during the COVID-19 outbreak. During the pandemic, a real-world data set was gathered that included about 100,000 Arabic tweets related to online learning. The overall goal is to use sentiment analysis of tweets to find patterns that help improve the quality of online learning. The data set that was collected has three classes: “Positive,” “Negative,” and “Neutral.” Crossvalidation is used to run the experiments ten times. Precision, recall, and F-measure was used to measure how well the algorithms worked. Classifiers, such as Support Vector Machines, K nearest neighbors, and Random Forest, were used to classify the dataset. Moreover, a detailed analysis and comparison of the results are made in this research. Finally, a visual examination of the data is made using the word cloud technique." @default.
- W4328011153 created "2023-03-22" @default.
- W4328011153 creator A5010508766 @default.
- W4328011153 creator A5012501249 @default.
- W4328011153 date "2022-12-17" @default.
- W4328011153 modified "2023-09-28" @default.
- W4328011153 title "Utilizing Sentiment Analysis to Enhance the Quality of Online Learning" @default.
- W4328011153 cites W1964940342 @default.
- W4328011153 cites W1973264851 @default.
- W4328011153 cites W2081475785 @default.
- W4328011153 cites W2795371086 @default.
- W4328011153 cites W2890269216 @default.
- W4328011153 cites W2916747500 @default.
- W4328011153 cites W3004283970 @default.
- W4328011153 cites W3010816311 @default.
- W4328011153 cites W3089030745 @default.
- W4328011153 cites W3201438617 @default.
- W4328011153 cites W3214973351 @default.
- W4328011153 cites W4200425003 @default.
- W4328011153 cites W4245471118 @default.
- W4328011153 cites W4285202075 @default.
- W4328011153 doi "https://doi.org/10.1109/nccc57165.2022.10067560" @default.
- W4328011153 hasPublicationYear "2022" @default.
- W4328011153 type Work @default.
- W4328011153 citedByCount "0" @default.
- W4328011153 crossrefType "proceedings-article" @default.
- W4328011153 hasAuthorship W4328011153A5010508766 @default.
- W4328011153 hasAuthorship W4328011153A5012501249 @default.
- W4328011153 hasConcept C111472728 @default.
- W4328011153 hasConcept C119857082 @default.
- W4328011153 hasConcept C12267149 @default.
- W4328011153 hasConcept C136764020 @default.
- W4328011153 hasConcept C138885662 @default.
- W4328011153 hasConcept C154945302 @default.
- W4328011153 hasConcept C169258074 @default.
- W4328011153 hasConcept C177264268 @default.
- W4328011153 hasConcept C199360897 @default.
- W4328011153 hasConcept C204321447 @default.
- W4328011153 hasConcept C2779530757 @default.
- W4328011153 hasConcept C2986087404 @default.
- W4328011153 hasConcept C41008148 @default.
- W4328011153 hasConcept C518677369 @default.
- W4328011153 hasConcept C58489278 @default.
- W4328011153 hasConcept C66402592 @default.
- W4328011153 hasConceptScore W4328011153C111472728 @default.
- W4328011153 hasConceptScore W4328011153C119857082 @default.
- W4328011153 hasConceptScore W4328011153C12267149 @default.
- W4328011153 hasConceptScore W4328011153C136764020 @default.
- W4328011153 hasConceptScore W4328011153C138885662 @default.
- W4328011153 hasConceptScore W4328011153C154945302 @default.
- W4328011153 hasConceptScore W4328011153C169258074 @default.
- W4328011153 hasConceptScore W4328011153C177264268 @default.
- W4328011153 hasConceptScore W4328011153C199360897 @default.
- W4328011153 hasConceptScore W4328011153C204321447 @default.
- W4328011153 hasConceptScore W4328011153C2779530757 @default.
- W4328011153 hasConceptScore W4328011153C2986087404 @default.
- W4328011153 hasConceptScore W4328011153C41008148 @default.
- W4328011153 hasConceptScore W4328011153C518677369 @default.
- W4328011153 hasConceptScore W4328011153C58489278 @default.
- W4328011153 hasConceptScore W4328011153C66402592 @default.
- W4328011153 hasLocation W43280111531 @default.
- W4328011153 hasOpenAccess W4328011153 @default.
- W4328011153 hasPrimaryLocation W43280111531 @default.
- W4328011153 hasRelatedWork W2979979539 @default.
- W4328011153 hasRelatedWork W3004897296 @default.
- W4328011153 hasRelatedWork W3127425528 @default.
- W4328011153 hasRelatedWork W3195168932 @default.
- W4328011153 hasRelatedWork W4205958290 @default.
- W4328011153 hasRelatedWork W4226073804 @default.
- W4328011153 hasRelatedWork W4311106074 @default.
- W4328011153 hasRelatedWork W4313150720 @default.
- W4328011153 hasRelatedWork W4327531511 @default.
- W4328011153 hasRelatedWork W4360772992 @default.
- W4328011153 isParatext "false" @default.
- W4328011153 isRetracted "false" @default.
- W4328011153 workType "article" @default.