Conceptual Framework: Leveraging Artificial Intelligence for Enhanced Travel Review Analysis and Insights

This study proposes an AI-based paradigm for travel review analysis. Travel reviews influence customer choices and the tourist business. The framework includes data collection and preprocessing, sentiment analysis, feature extraction and topic modeling, deep learning, and insight production. Data collection and preparation provide diversified, high-quality trip review data for analysis. Sentiment analysis and opinion mining classify reviews to determine travelers happiness. Feature extraction and topic modeling reveal traveler preferences and interests by extracting keywords, topics, and features from reviews. Deep learning methods like CNN and LSTM networks capture complicated travel review structures and dependencies, improving sentiment analysis and opinion mining. These methods help contextualize passengers feelings and preferences throughout time. The proposed approach yields travel industry-wide information. They assist in data-driven decision-making, personalized suggestions, destination marketing and management strategies, service quality, market research and competitive analysis, and product creation. AI travel review analysis presents obstacles and considerations. These include data quality and availability, privacy and ethical considerations, bias mitigation and fairness, interpretability and explainability, technical skills and resources, user acceptability and adoption, and continual learning. By adopting the proposed conceptual framework and overcoming the obstacles, travel companies can gain deeper insights from travel reviews, improve customer experiences, and make informed decisions to stay competitive in the dynamic and growing travel market. Travel review analysis, artificial intelligence, conceptual framework, sentiment analysis, opinion mining, deep learning, data collection, preprocessing, feature extraction, topic modeling, insights generation, decision-making, personalized recommendations, destination marketing.