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The proliferation of user-generated content on social networks and websites has heightened the significance of sentiment analysis, also known as opinion mining, as a critical tool for comprehending people's attitudes toward various topics. Aspect-level sentiment analysis, which considers specific aspects or features of texts, provides a more comprehensive view of sentiment analysis. The aspect-level approach encompasses both explicit and implicit aspects, where explicit aspects are readily mentioned in texts while implicit aspects are implied or inferred from contextual clues. Despite the significance of implicit aspects in the overall review, previous research has predominantly focused on explicit aspect extraction. Limited attention has been given to the extraction of implicit aspects, despite their potential impact on capturing the complete sentiment picture of texts. Therefore, this study aims to find an aspect extraction solution capable of identifying and extracting both explicit and implicit aspects from texts. This study compares various machine and deep learning models on the SemEval-2014 and SemEval-2016 restaurant datasets. The experimental analysis demonstrates that the proposed Aspect-BiLSTM model emerged as the best-performing model, achieving high accuracy in classifying both explicit and implicit aspects, with 92.9% accuracy for the 2014 and 90.7% accuracy for the 2016 datasets. Notably, the proposed solution was able to capture multiple aspects of texts, making it more robust and versatile. This study highlights the efficacy of the Aspect-BiLSTM model for aspect extraction, which will give valuable insights into the advancement of aspect-level sentiment analysis. Doi: 10.28991/ESJ-2024-08-01-05 Full Text: PDF

Diabetes mellitus is one of medical science's most important research topics because of the disease's severe consequences. High blood glucose levels characterize it. Early detection of diabetes is made possible by machine learning techniques with their intelligent capabilities to accurately predict diabetes and prevent its complications. Therefore, this study aims to find a machine learning approach that can more accurately predict diabetes. This study compares the performance of various classical machine learning models with the hybrid machine learning approach. The hybrid model includes the homogenous model, which comprises Random Forest, AdaBoost, XGBoost, Extra Trees, Gradient Booster, and the heterogeneous model that uses stacking ensemble methods. The stacking ensemble or stacked generalization approach is a meta-classifier in which multiple learners collaborate for prediction. The performance of the homogeneous hybrid models, Stacked Generalization and the classic machine learning methods such as Naive Bayes and Multilayer Perceptron, k-Nearest Neighbour, and support vector machine are compared. The experimental analysis using Pima Indians and the early-stage diabetes dataset demonstrates that the hybrid models achieve higher accuracy in diagnosing diabetes than the classical models. In the comparison of all the hybrid models, the heterogeneous model using the Stacked Generalization approach outperformed other models by achieving 83.9% and 98.5%. Doi: 10.28991/ESJ-2023-07-01-08 Full Text: PDF