AI Based Knowledge Representation on Spam Review Detection by Bottom -Up Learning Approach

Abstract

A bottom-up learning approach for AI-based knowledge representation in spam review detection utilizes machine learning and natural language processing to uncover fraudulent reviews. This method begins with raw data, identifying patterns and extracting features from text, metadata, and user behaviour. Unlike traditional rule-based top-down techniques, it gradually builds knowledge through unsupervised or semi-supervised learning, enhancing accuracy over time. Deep learning, sentiment analysis, and clustering techniques aid in distinguishing authentic from fake reviews. By continuously adapting to new data, this approach improves spam detection, making it more effective against evolving deceptive tactics on online platforms. Detecting spam reviews is crucial for maintaining the integrity of online platforms. AI-based knowledge representation using a bottom-up learning approach provides an effective solution for identifying fraudulent reviews. Unlike traditional rule-based methods that depend on predefined patterns, this approach begins with raw data and gradually builds knowledge by analysing text, metadata, and user behaviour leveraging unsupervised or semi-supervised learning, the system enhances its accuracy over time, allowing it to adapt to evolving spam tactics. Techniques such as deep learning, sentiment analysis, and clustering help differentiate authentic from fake reviews by examining writing styles, behavioural trends, and sentiment inconsistencies. This enables the model to detect deceptive reviews more effectively. A key advantage of this bottom-up approach is its ability to continuously learn from new data, making it more dynamic than static rule-based systems. It refines its detection mechanisms in real time, helping platforms stay ahead of increasingly sophisticated spam techniques. The adaptability of AI-driven models ensures that fraudulent reviews are identified with greater precision, fostering trust in online reviews across e-commerce, travel, and service-oriented platforms. In summary, AI-based knowledge representation through a bottom-up learning approach offers a scalable and intelligent method for spam detection. Its capacity to evolve with emerging threats makes it an essential tool for preserving the authenticity of online reviews, enhancing transparency, and boosting consumer confidence in digital marketplaces.