Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385246395> ?p ?o ?g. }
Showing items 1 to 83 of
83
with 100 items per page.
- W4385246395 abstract "This study strives to investigate the effectiveness of ensemble learning methods in analyzing the sentiments of menopausal experiences as manifested on Twitter. To achieve this objective, we leveraged data crawling techniques to collect pertinent Twitter data from two different time periods i.e., February 2023 and March 2023, which we analyzed using ensemble learning approaches. By doing so, we aimed to augment the precision and robustness of our sentiment analysis results. Specifically, we collected relevant Twitter data relating to menopause by using specific keywords and applied the term frequency-inverse document frequency (TF-IDF) algorithm to extract features. However, our dataset exhibited class imbalance, which we addressed using the Synthetic Minority Over-sampling Technique (SMOTE). Subsequently, we trained several ensemble learning models, including bagging, boosting, and random forest, using the sci-kit-learn library in Python. We evaluated the efficacy of each model using accuracy, recall and precision as the performance metric. Our analysis demonstrated that the Random Forest algorithm outperformed other ensemble learning approaches in terms of accuracy, attaining an accuracy of 0.96 and 0.89 respectively for the two datasets collected. Our research is of paramount importance as it provides a comprehensive understanding of the emotional experiences of women during menopause. We underscore the significance of mood changes in comprehending the emotional experiences of menopausal women. Our results can inform the development of personalized interventions for managing menopausal symptoms, which can significantly enhance the quality of life of women undergoing menopause." @default.
- W4385246395 created "2023-07-26" @default.
- W4385246395 creator A5019119813 @default.
- W4385246395 creator A5032010496 @default.
- W4385246395 creator A5082011088 @default.
- W4385246395 creator A5092539854 @default.
- W4385246395 date "2023-06-29" @default.
- W4385246395 modified "2023-10-16" @default.
- W4385246395 title "A Multipronged Approach for Modeling Menopausal Health Using Ensemble Learning" @default.
- W4385246395 cites W2941799245 @default.
- W4385246395 cites W2951494616 @default.
- W4385246395 cites W2996665118 @default.
- W4385246395 cites W2998204065 @default.
- W4385246395 cites W3000739907 @default.
- W4385246395 cites W3003618396 @default.
- W4385246395 cites W3039503982 @default.
- W4385246395 cites W3081987387 @default.
- W4385246395 cites W3090154305 @default.
- W4385246395 cites W3102944297 @default.
- W4385246395 cites W3105951585 @default.
- W4385246395 cites W3131504710 @default.
- W4385246395 cites W3160554854 @default.
- W4385246395 cites W3169769950 @default.
- W4385246395 cites W3193706068 @default.
- W4385246395 cites W4223999294 @default.
- W4385246395 cites W4229049385 @default.
- W4385246395 cites W4282921149 @default.
- W4385246395 cites W4286206444 @default.
- W4385246395 cites W4296333182 @default.
- W4385246395 cites W4297499142 @default.
- W4385246395 cites W4313213437 @default.
- W4385246395 cites W3184790954 @default.
- W4385246395 doi "https://doi.org/10.1109/icest58410.2023.10187376" @default.
- W4385246395 hasPublicationYear "2023" @default.
- W4385246395 type Work @default.
- W4385246395 citedByCount "0" @default.
- W4385246395 crossrefType "proceedings-article" @default.
- W4385246395 hasAuthorship W4385246395A5019119813 @default.
- W4385246395 hasAuthorship W4385246395A5032010496 @default.
- W4385246395 hasAuthorship W4385246395A5082011088 @default.
- W4385246395 hasAuthorship W4385246395A5092539854 @default.
- W4385246395 hasConcept C104317684 @default.
- W4385246395 hasConcept C118552586 @default.
- W4385246395 hasConcept C119857082 @default.
- W4385246395 hasConcept C154945302 @default.
- W4385246395 hasConcept C15744967 @default.
- W4385246395 hasConcept C169258074 @default.
- W4385246395 hasConcept C185592680 @default.
- W4385246395 hasConcept C2780733359 @default.
- W4385246395 hasConcept C41008148 @default.
- W4385246395 hasConcept C45942800 @default.
- W4385246395 hasConcept C46686674 @default.
- W4385246395 hasConcept C55493867 @default.
- W4385246395 hasConcept C63479239 @default.
- W4385246395 hasConceptScore W4385246395C104317684 @default.
- W4385246395 hasConceptScore W4385246395C118552586 @default.
- W4385246395 hasConceptScore W4385246395C119857082 @default.
- W4385246395 hasConceptScore W4385246395C154945302 @default.
- W4385246395 hasConceptScore W4385246395C15744967 @default.
- W4385246395 hasConceptScore W4385246395C169258074 @default.
- W4385246395 hasConceptScore W4385246395C185592680 @default.
- W4385246395 hasConceptScore W4385246395C2780733359 @default.
- W4385246395 hasConceptScore W4385246395C41008148 @default.
- W4385246395 hasConceptScore W4385246395C45942800 @default.
- W4385246395 hasConceptScore W4385246395C46686674 @default.
- W4385246395 hasConceptScore W4385246395C55493867 @default.
- W4385246395 hasConceptScore W4385246395C63479239 @default.
- W4385246395 hasLocation W43852463951 @default.
- W4385246395 hasOpenAccess W4385246395 @default.
- W4385246395 hasPrimaryLocation W43852463951 @default.
- W4385246395 hasRelatedWork W2955385375 @default.
- W4385246395 hasRelatedWork W2964528625 @default.
- W4385246395 hasRelatedWork W3044867970 @default.
- W4385246395 hasRelatedWork W3100297620 @default.
- W4385246395 hasRelatedWork W3159962567 @default.
- W4385246395 hasRelatedWork W3208169454 @default.
- W4385246395 hasRelatedWork W4281560664 @default.
- W4385246395 hasRelatedWork W4313906961 @default.
- W4385246395 hasRelatedWork W4382315444 @default.
- W4385246395 hasRelatedWork W4385728794 @default.
- W4385246395 isParatext "false" @default.
- W4385246395 isRetracted "false" @default.
- W4385246395 workType "article" @default.