Matches in SemOpenAlex for { <https://semopenalex.org/work/W3212135906> ?p ?o ?g. }
Showing items 1 to 87 of
87
with 100 items per page.
- W3212135906 endingPage "79" @default.
- W3212135906 startingPage "79" @default.
- W3212135906 abstract "Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained. In this paper, a comprehensive comparative analysis of various hyperparameter tuning techniques is performed; these are Grid Search, Random Search, Bayesian Optimization, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). They are used to optimize the accuracy of six machine learning algorithms, namely, Logistic Regression (LR), Ridge Classifier (RC), Support Vector Machine Classifier (SVC), Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB) classifiers. To test the performance of each hyperparameter tuning technique, the machine learning models are used to solve an Arabic sentiment classification problem. Sentiment analysis is the process of detecting whether a text carries a positive, negative, or neutral sentiment. However, extracting such sentiment from a complex derivational morphology language such as Arabic has been always very challenging. The performance of all classifiers is tested using our constructed dataset both before and after the hyperparameter tuning process. A detailed analysis is described, along with the strengths and limitations of each hyperparameter tuning technique. The results show that the highest accuracy was given by SVC both before and after the hyperparameter tuning process, with a score of 95.6208 obtained when using Bayesian Optimization." @default.
- W3212135906 created "2021-11-22" @default.
- W3212135906 creator A5031767757 @default.
- W3212135906 creator A5037282062 @default.
- W3212135906 creator A5065045459 @default.
- W3212135906 creator A5090721582 @default.
- W3212135906 date "2021-11-17" @default.
- W3212135906 modified "2023-10-11" @default.
- W3212135906 title "Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis" @default.
- W3212135906 cites W1981976602 @default.
- W3212135906 cites W1982147649 @default.
- W3212135906 cites W1990771923 @default.
- W3212135906 cites W2127613773 @default.
- W3212135906 cites W2175269250 @default.
- W3212135906 cites W2527291902 @default.
- W3212135906 cites W2560103205 @default.
- W3212135906 cites W2593742786 @default.
- W3212135906 cites W2737990573 @default.
- W3212135906 cites W2907502017 @default.
- W3212135906 cites W2951925285 @default.
- W3212135906 cites W3004829062 @default.
- W3212135906 cites W3035615221 @default.
- W3212135906 cites W3045004532 @default.
- W3212135906 cites W3126489268 @default.
- W3212135906 cites W3162843419 @default.
- W3212135906 cites W4247505106 @default.
- W3212135906 doi "https://doi.org/10.3390/informatics8040079" @default.
- W3212135906 hasPublicationYear "2021" @default.
- W3212135906 type Work @default.
- W3212135906 sameAs 3212135906 @default.
- W3212135906 citedByCount "80" @default.
- W3212135906 countsByYear W32121359062022 @default.
- W3212135906 countsByYear W32121359062023 @default.
- W3212135906 crossrefType "journal-article" @default.
- W3212135906 hasAuthorship W3212135906A5031767757 @default.
- W3212135906 hasAuthorship W3212135906A5037282062 @default.
- W3212135906 hasAuthorship W3212135906A5065045459 @default.
- W3212135906 hasAuthorship W3212135906A5090721582 @default.
- W3212135906 hasBestOaLocation W32121359061 @default.
- W3212135906 hasConcept C10485038 @default.
- W3212135906 hasConcept C11413529 @default.
- W3212135906 hasConcept C119857082 @default.
- W3212135906 hasConcept C12267149 @default.
- W3212135906 hasConcept C154945302 @default.
- W3212135906 hasConcept C169258074 @default.
- W3212135906 hasConcept C2778049539 @default.
- W3212135906 hasConcept C41008148 @default.
- W3212135906 hasConcept C52001869 @default.
- W3212135906 hasConcept C84525736 @default.
- W3212135906 hasConcept C85617194 @default.
- W3212135906 hasConcept C8642999 @default.
- W3212135906 hasConcept C95623464 @default.
- W3212135906 hasConceptScore W3212135906C10485038 @default.
- W3212135906 hasConceptScore W3212135906C11413529 @default.
- W3212135906 hasConceptScore W3212135906C119857082 @default.
- W3212135906 hasConceptScore W3212135906C12267149 @default.
- W3212135906 hasConceptScore W3212135906C154945302 @default.
- W3212135906 hasConceptScore W3212135906C169258074 @default.
- W3212135906 hasConceptScore W3212135906C2778049539 @default.
- W3212135906 hasConceptScore W3212135906C41008148 @default.
- W3212135906 hasConceptScore W3212135906C52001869 @default.
- W3212135906 hasConceptScore W3212135906C84525736 @default.
- W3212135906 hasConceptScore W3212135906C85617194 @default.
- W3212135906 hasConceptScore W3212135906C8642999 @default.
- W3212135906 hasConceptScore W3212135906C95623464 @default.
- W3212135906 hasIssue "4" @default.
- W3212135906 hasLocation W32121359061 @default.
- W3212135906 hasLocation W32121359062 @default.
- W3212135906 hasOpenAccess W3212135906 @default.
- W3212135906 hasPrimaryLocation W32121359061 @default.
- W3212135906 hasRelatedWork W2200000192 @default.
- W3212135906 hasRelatedWork W2395916875 @default.
- W3212135906 hasRelatedWork W2405673391 @default.
- W3212135906 hasRelatedWork W2920302751 @default.
- W3212135906 hasRelatedWork W2963001956 @default.
- W3212135906 hasRelatedWork W2966761695 @default.
- W3212135906 hasRelatedWork W3103707007 @default.
- W3212135906 hasRelatedWork W3169687406 @default.
- W3212135906 hasRelatedWork W3206613651 @default.
- W3212135906 hasRelatedWork W4286902601 @default.
- W3212135906 hasVolume "8" @default.
- W3212135906 isParatext "false" @default.
- W3212135906 isRetracted "false" @default.
- W3212135906 magId "3212135906" @default.
- W3212135906 workType "article" @default.