Fine-Tuning BERT and ALBERT for Sentiment Analysis in Brand Reputation Monitoring Using Amazon Data

In today's digital marketplace, where customer perception may have a big impact on business performance, brand reputation monitoring is essential. The BERT and ALBERT models' fine-tuning for sentiment analysis on Amazon review data is investigated in this work. We assess their efficacy in managing brand reputation and their ability to recognize sentiment polarity. According to experimental results, ALBERT's effective architecture allows it to attain competitive accuracy while maintaining computational efficiency. According to our research, transformer-based models outperform conventional machine learning methods in sentiment categorization and offer reliable results. Practical applications for companies looking to improve their reputation monitoring techniques are also covered in this study