Article
ReviewSenseNet: Dual-Task Transformer Learning for Title Classification and Rating Prediction
Online mobile technology reviews have grown rapidly, with millions of new reviews posted each month and more than 80 percent of consumers relying on them before making purchase decisions. The large volume and unstructured nature of these reviews create significant challenges for timely and consistent analysis, particularly for high-demand products such as iPhones. Automated analysis of mobile tech reviews is essential for application scenarios such as customer sentiment monitoring, product quality assessment, market trend analysis, and decision support for both consumers and manufacturers. Accurate interpretation of review titles and ratings can directly influence purchasing behavior and product improvement strategies. Traditional manual review analysis suffers from high time consumption, human bias, poor scalability, and inconsistency when handling large-scale datasets. These limitations make it difficult to extract reliable insights or identify hidden sentiment patterns from massive collections of mobile tech reviews. To address these challenges, this work proposes a Dual-Target Efficiently Learning an Encoder That Classifies Token Replacements Accurately (ELECTRA) prediction of iPhone mobile tech reviews. The approach leverages an NLP-based dataset of mobile tech reviews and employs the ELECTRA transformer model for contextual text representation. Multiple machine learning classifiers, including AdaBoost Classifier, Tree Alternative Optimization (TAO) Tree Classifier, and Extra Tree Classifier, are integrated to enhance predictive performance and enable comparative evaluation. The unified framework processes review text efficiently and generates two outputs within a single pipeline: classification of review titles and prediction of user ratings. The proposed system improves analytical accuracy, reduces manual effort, and provides scalable, reliable insights for mobile technology review analysis.
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