A Multipronged Approach for Modeling Menopausal Health Using Ensemble Learning

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.