Enhancing Mobile Edge Computing customer reviews analysis with Ensemble Sparse Support Vector L1 Regularization Based Crossover Discrete Mycorrhized Algorithm

Abstract

Mobile Edge Computing has emerged as a transformative technology, enhancing the efficiency of internet community platforms by enabling real-time data processing and analysis at the network’s edge. These platforms facilitate the exchange of user opinions and ideas, providing valuable insights into user attitudes and preferences. Despite advancements in Mobile Edge Computing, challenges persist in effectively categorizing customer reviews due to latency, data scarcity, and overfitting issues in computational models. This study integrates natural language processing techniques with edge computing infrastructure to analyze reviews closer to their source, thereby minimizing latency and improving overall performance. To enhance the detection accuracy in Mobile Edge customer reviews, this paper proposes an innovative approach called the Ensemble Sparse Support Vector L1 Regularization-based Crossover Discrete Mycorrhized Algorithm. Pre-processing steps, such as tokenization, lemmatization, and stemming, are employed to improve data quality. Feature extraction is performed using a stacked autoencoder, which incorporates multiple layers to address data scarcity issues. To optimize the performance of the Sparse Support Vector L1 Regularization, a novel Crossover Discrete Mycorrhized Optimization Algorithm is introduced, mitigating overfitting and improving classification accuracy. The proposed approach is validated using datasets such as the Mobile Recommendation System Dataset, Mobile Positioning Dataset, Mobile Edge Distance Analysis Dataset, International Phone Checker API, and Financial Fraud Detection Dataset. Experimental results demonstrate superior performance, achieving 98.52% accuracy, 98.39% precision, 98.28% recall, and 98.21% F1-score in aspect category detection. The proposed method addresses critical gaps in latency reduction and data processing accuracy in MEC environments by significantly improving reliability and efficiency in customer review analysis. These findings contribute to more robust, customer-centric Mobile Edge Computing systems, fostering enhanced real-time decision-making and user experience.