Advancing recommendation systems with DeepMF and hybrid sentiment analysis: Deep learning and Lexicon-based integration

In the hotel industry, ensuring customer satisfaction and providing personalized recommendations are crucial elements for creating a remarkable guest experience. However, traditional recommendation systems encounter several challenges that hinder their effectiveness. These challenges include cold start problems, where it is difficult to make recommendations for new or less-rated items, as well as data sparsity, which limits the availability of relevant information. Additionally, accurately interpreting the diverse sentiments expressed by customers in their reviews poses another significant challenge. This study tackles these challenges by integrating sentiment analysis into hotel recommendation systems, aiming to capture and analyze guest opinions and sentiments from their reviews. This study aims to enhance recommendation systems by integrating a hybrid sentiment analysis approach. The approach combines lexicon-based techniques and deep learning methodologies, using TextBlob with Bag of Words and a Multilayer Perceptron (MLP) algorithm to analyze the sentiment of textual data. The hybrid sentiment analysis approach exhibits an impressive accuracy rate of 88.63%, demonstrating its effectiveness in capturing sentiment from customer reviews. This integration enables recommendation systems to better understand and incorporate customer sentiments, leading to improved personalized recommendations. Moreover, we combine this hybrid sentiment analysis with DeepMF for collaborative hotel recommendations, which yields a remarkable Root Mean Square Error (RMSE) of 0.1. By integrating sentiment analysis into the recommendation system, we gain valuable insights into customer preferences, leading to improved recommendation quality and personalization. This research highlights the potential of sentiment analysis in optimizing customer experience management within the hotel industry, providing a valuable tool for enhancing guest satisfaction and engagement.