Practical Example: Transfer Learning for Sentiment Analysis
Автор: NextGen AI & Tech Explorer
Загружено: 2025-05-20
Просмотров: 7
@genaiexp Sentiment analysis is a popular NLP task that involves determining the sentiment expressed in text, such as positive, negative, or neutral. Transfer learning enhances sentiment analysis by leveraging pre-trained models like BERT, which already understand language patterns. Implementing this involves selecting a sentiment analysis dataset, such as IMDB reviews, and fine-tuning the BERT model on this dataset. This approach not only improves accuracy but also reduces training time compared to training a model from scratch. Real-world applications of sentiment analysis include monitoring customer feedback, analyzing social media trends, and enhancing user experience in recommendation systems, showcasing the impact of transfer learning in practical scenarios.
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