BERT-based Sentiment Analysis and Rating Prediction System for Hotel Reviews

This article introduces an end-to-end hotel review analysis system built using the BERT pre-trained model, aiming to solve the problems of sentiment classification and rating prediction under massive review data. The system covers the complete workflow including data preprocessing, feature extraction, model construction, and training. Through BERT's powerful semantic understanding capabilities, it provides data-driven insights for the hotel industry, helping managers quickly grasp users' emotional tendencies and satisfaction levels.

In the digital age, online reviews are an important reference for consumer decisions, and the hotel industry relies on them to build credibility. However, under massive reviews, traditional rules or shallow machine learning methods struggle to capture deep semantics and contextual relationships. The emergence of pre-trained models like BERT has brought changes to this field. This project aims to build an end-to-end system that uses BERT to implement sentiment classification and rating prediction, automatically learning complex patterns in reviews.

BERT (Bidirectional Encoder Representations from Transformers) was proposed by Google in 2018. Its core innovations include: 1. Bidirectional encoding: Learning bidirectional semantics through the Masked Language Model (MLM) task; 2. Next Sentence Prediction (NSP): Understanding relationships between sentences; 3. Transfer learning: Pre-trained weights can be quickly adapted to specific domains through fine-tuning, reducing data and computing resource requirements.

The system is divided into four layers: 1. Data layer: Responsible for review data collection and preprocessing (cleaning, tokenization, augmentation, segmentation); 2. Feature layer: Using BERT to convert text into high-dimensional semantic vectors (extracting [CLS] token embedding or the last layer's hidden state); 3. Model layer: Includes sentiment classification (multi-classification, fully connected + Softmax) and rating prediction (multi-classification or regression); 4. Application layer: Provides RESTful API, batch processing, and visual dashboard.

1. Text preprocessing and vectorization: Using the Hugging Face Transformers library to load the BERT model and tokenizer, generate tensors like input_ids, and extract [CLS] embedding; 2. Model fine-tuning strategy: Using a small learning rate (e.g., 2e-5) for BERT layers, a larger learning rate for classification layers, freezing the first few layers, and combining early stopping and adversarial training; 3. Loss functions: Cross-entropy for sentiment classification, MSE or cross-entropy for rating prediction.

Evaluation metrics include accuracy, precision, recall, F1 score, and confusion matrix. Experiments show: 92-95% accuracy for binary sentiment classification, 70-75% accuracy for 5-star rating prediction (exact match), and MAE of 0.3-0.5 stars. Compared to traditional models (SVM, Naive Bayes), the F1 score is improved by 5-10%.

The system can be applied to: 1. Real-time public opinion monitoring: Marking low-rated reviews and reminding customer service to respond; 2. Service quality improvement: Identifying service shortcomings (e.g., cleaning issues); 3. Competitor analysis: Understanding the strengths and weaknesses of competing products; 4. Personalized recommendation: Combining user preferences to improve conversion rates.

Current challenges: Insufficient multilingual support, limitations in sarcasm and irony recognition, and the need to improve the domain adaptability of general BERT. Future improvement directions: Domain pre-training (hotel corpus), multimodal fusion (images + behavior data), enhanced interpretability (attention visualization), and real-time learning mechanisms. Summary: This system demonstrates the potential of pre-trained models in vertical domains and will play a more important role in the service industry in the future.