A Comprehensive Review of Advanced Intelligent Systems for Website Review Analysis

Abstract:-The rapid expansion of digital platforms has led to an overwhelming volume of user-generated reviews, making it crucial to develop intelligent systems for efficient analysis and sentiment classification. Website reviews serve as a key factor in shaping consumer trust, influencing purchasing decisions, and determining online reputation. Traditional review analysis methods often struggle with handling large datasets, detecting nuanced sentiments, and differentiating between authentic and biased feedback. This paper provides a comprehensive review of advanced intelligent systems for website review analysis, focusing on cutting-edge techniques such as natural language processing (NLP), machine learning (ML), deep learning (DL), and sentiment analysis models. We explore various methodologies, including supervised and unsupervised learning algorithms, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models such as BERT, which have demonstrated superior accuracy in opinion mining. Additionally, we examine the role of opinion lexicons, hybrid AI models, and feature extraction techniques in improving classification performance. Furthermore, this study highlights the challenges in review analysis, including fake review detection, multilingual sentiment analysis, sarcasm detection, and domain-specific adaptability. We compare the effectiveness of different approaches in addressing these challenges and discuss their real-world applications in e-commerce, social media monitoring, and brand reputation management

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