Opinion Mining for Product Reviews Using Artificial Intelligence

The rapid growth of e-commerce platforms has resulted in a large volume of customer-generated product reviews. These reviews contain valuable information about customer opinions, satisfaction levels, product quality, and specific product features. However, manually analysing a large number of reviews is time-consuming and difficult. Opinion mining, also known as sentiment analysis, uses Artificial Intelligence and Natural Language Processing techniques to automatically identify and classify opinions expressed in textual reviews. This paper presents a conceptual AI-based framework for analysing product reviews and identifying their sentiment polarity as positive, negative, or neutral. The proposed approach combines contextual language representation using Bidirectional Encoder Representations from Transformers (BERT) with aspect-based sentiment analysis inspired by Aspect-specific Graph Convolutional Networks (ASGCN). The system performs review preprocessing, contextual feature extraction, aspect identification, sentiment classification, and generation of an overall opinion summary. The reviewed literature also includes hybrid BERT-CNN architectures, user/context-aware graph modelling, and multimodal learning approaches, which provide additional directions for improving opinion analysis. The proposed framework aims to provide more detailed and meaningful information than simple positive or negative classification by identifying the product aspects responsible for customer opinions. Such a system can assist customers in decision-making and help businesses understand product strengths, weaknesses, and areas requiring improvement