Deep Learning Sentiment Analysis of Hotel Reviews with BiLSTM

In the digital era, the rapid growth of online platforms has significantly transformed the hospitality industry, where customer decisions are increasingly influenced by user-generated reviews. These reviews provide valuable insights into customer experiences; however, the vast volume of unstructured textual data makes manual analysis inefficient and impractical. To address this challenge, this study proposes an automated sentiment analysis system using deep learning techniques to classify hotel reviews into positive and negative sentiments.The research utilizes a large-scale dataset comprising over 500,000 hotel reviews, which undergoes extensive preprocessing, including text cleaning, tokenization, stopword removal, and data balancing to ensure model reliability. Exploratory Data Analysis (EDA) is conducted to understand data distribution and extract meaningful patterns. The processed textual data is then transformed into numerical representations using tokenization and sequence padding techniques.Two deep learning models, Long Short-Term Memory (LSTM) and Bidirectional Long ShortTerm Memory (BiLSTM), are implemented to capture sequential dependencies and contextual relationships.