A Comprehensive Review of Advanced Intelligent Systems for Website Review Analysis
Volume: 13 - Issue: 12 - Date: 01-12-2024
Approved ISSN: 2278-1412
Published Id: IJAECESTU432 | Page No.: 101-104
Author: Syed Fazal Ur Rahman
Co- Author: Jeetendra Singh Yadav,,,
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
Key Words:-Sentiment Analysis, Website Review Analysis, Natural Language Processing (NLP), Machine Learning, Deep Learning, Fake Review Detection, Opinion Mining, Text Classification
Area:-Engineering
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