A Robust Agentic Pipeline for Dual-Target Classification of Mobile User Reviews and Ratings

Abstract

Mobile reviews act as a key indicator of customer satisfaction, with over 90% of smartphone users referring to reviews before purchasing a device, and 72% of consumers indicating that positive feedback enhances their trust in a brand. However, manual examination of customer reviews and ratings is inefficient, susceptible to errors, and often fails to capture the subtle emotions present in unstructured text. To address these issues, this study proposes a framework based on Natural Language Processing (NLP) using an iPhone 14 dataset that includes user reviews, titles, and ratings. The workflow begins with NLP preprocessing and Exploratory Data Analysis (EDA) to clean, standardize, and visualize the data distribution. Next, Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA) is utilized for contextual feature extraction, enabling effective semantic representation of textual data. To handle class imbalance in review categories, K-Means Synthetic Minority Over-Sampling Technique (K-Means SMOTE) is applied to generate synthetic samples. Unlike existing approaches such as Adaptive Boosting Classifier (ABC) and Tao Tree Classifier (TTC), the proposed framework integrates an Extra Trees Classifier (ETC) to ensure more robust and scalable classification. The model predicts bivariate outputs Review Title and Rating thereby improving both sentiment understanding and rating consistency. By automating the analysis of reviews, the system provides valuable insights into customer satisfaction, product performance, and brand perception, ultimately supporting better decision-making and enhancing the overall customer experience.