Semantic Features-Based Discourse Analysis Using Deceptive and Real Text Reviews

Social media usage for news, feedback on services, and even shopping is increasing. Hotel food cleanliness staff behavior are also discussed online. Hotels reviewed by the public via comments their websites social accounts. This assists potential customers before they book services of a hotel, but it creates an opportunity abuse. Scammers leave deceptive reviews regarding never received, or inject fake promotions to lower ranking competitors. These malicious attacks will only increase in future become serious problem not merchants hotel customers. To rectify problem, many artificial intelligence–based studies have performed discourse analysis validate genuineness. However, still challenge find precise, robust, deployable automated solution perform analysis. A credibility check would help create safer environment. The proposed study conducted real automatically. It uses dataset reviews, containing both positive negative reviews. Under investigation hypothesis that strong, fact-based, realistic words used truthful whereas lack coherent, structural context. Therefore, frequency weight–based semantically aware features were study, comparative was performed. shown strength against current hypothesis. Further, holdout k-fold methods applied validation methods. final results indicate inspire more confidence detect deception text.