Arabic Hotel Reviews Sentiment Analysis Using Deep Learning
The British University in Dubai (BUiD)
Arabic Hotel Feedback sentiment analysis plays a significant role in understanding the opinions and sentiments expressed by customers in their reviews. With the growing popularity of online platforms and social media, Arabic Hotel Feedback have become a valuable source of information for both hotel owners and potential customers. Sentiment analysis techniques aim to automatically classify the sentiment polarity of these reviews as positive, negative, or neutral, providing valuable insights into customer satisfaction and areas of improvement for hotels. In this study, we present a comprehensive analysis of Arabic Hotel Reviews sentiment analysis. We collected a large dataset of Arabic hotel Feedback from various online platforms, encompassing a wide range of hotels and customer experiences. The dataset was carefully annotated with sentiment labels by human annotators to serve as ground truth for training and evaluation purposes. We employed state-of-the-art machine learning and natural language processing techniques to develop sentiment analysis models specifically tailored for the Arabic language. Our models utilized advanced text preprocessing, feature extraction, and classification algorithms to accurately predict sentiment polarity in Arabic hotel reviews. We evaluated the performance of our models using various evaluation metrics, including accuracy, precision, recall, and F1-score, to assess their effectiveness in sentiment classification. The results of our study demonstrate the viability and effectiveness of sentiment analysis in Arabic Hotel Reviews. Our models achieved high accuracy and robust performance in sentiment classification, enabling hotel owners to gain valuable insights into customer sentiments and make informed decisions to enhance customer satisfaction and improve their services. CNN model demonstrated superior performance in terms of precision, recall, F1-score, and accuracy, consistently achieving a score of 74% across all evaluation metrics. The SVM model closely followed with a score of 73% for the same metrics. The LSTM model exhibited slightly lower performance, achieving values between 70% and 71%. On the other hand, the DT model had the lowest scores among all the models, with values of 66% and 68%. The findings of this study contribute to the growing body of research in sentiment analysis and provide valuable insights into sentiment patterns specific to Arabic hotel reviews. Overall, this study highlights the importance of sentiment analysis in the context of Arabic Hotel Feedback and provides a foundation for future research and applications in the field. The insights gained from sentiment analysis can empower hotel owners, marketers, and decision-makers to better understand customer sentiments, address concerns, and optimize their services to meet customer expectations in the dynamic and competitive hotel industry.
Arabic hotel reviews, sentiment analysis, deep learning, customer satisfaction, natural language processing, customer sentiments, hotel industry