Aspect-Based Sentiment Analysis (ABSA) has become an integral component of Natural Language Processing (NLP). It provides comprehensive insights into individuals' sentiments regarding specific aspects. This study conducts a comprehensive bibliometric analysis of 913 journal articles published from 2010 to 2024, sourced from the Scopus database, to examine trends, challenges, and prospective directions in ABSA research. The research examines the expansion of publications, citation metrics, and scholarly networks. It integrates performance analysis metrics (such as h-index, g-index, and citations per paper) with sophisticated science mapping techniques, including keyword co-occurrence networks, thematic evolution, and thematic mapping. This novel integrated approach, rarely employed in prior ABSA bibliometric studies, reveals both historical trends and emerging niche themes that remain underexplored. The findings illustrate the evolution of ABSA from rule-based methodologies to transformer-based architectures applicable in e-commerce, social media, and customer feedback systems. Key issues identified include multilingual adaptability, implicit sentiment detection, and cross-domain scalability. The research indicates that global institutions have significantly contributed, yet productivity and impact differ markedly across nations. Multimodal analysis, transfer learning, and contextualized models such as BERT are pivotal contemporary methodologies that can assist in addressing existing challenges. Future research should concentrate on integrating diverse disciplines, analyzing data across various languages and modalities, and developing scalable and comprehensible models. This study contributes to the domains of artificial intelligence and sentiment analysis by offering a comprehensive and data-driven overview of the ABSA landscape. It accomplishes this by providing both strategic insights and methodological enhancements.

The study was aimed to create a predictive model for predicting students’ academic performance based on a neural network algorithm. This is because recently, educational data mining has become very helpful in decision making in an educational context and hence improving students’ academic outcomes. This study implemented a Neural Network algorithm as a data mining technique to extract knowledge patterns from student’s dataset consisting of 480 instances (students) with 16 attributes for each student. The classification metric used is accuracy as the model quality measurement. The accuracy result was below 60% when the Adam model optimizer was used. Although, after applying the Stochastic Gradient Descent optimizer and dropout technique, the accuracy increased to more than 75%. The final stable accuracy obtained was 76.8% which is a satisfactory result. This indicates that the suggested NN model can be reliable for prediction, especially in social science studies.