Sentiment Analysis on Any Length of Text With Transformers - Python

Sentiment Analysis on Any Length of Text With Transformers - Python

Learn how to perform sentiment analysis on long text using transformer models in Python. Explore techniques to overcome the token limit constraints of popular models like BERT when processing extensive content such as news articles or social media posts. Discover a step-by-step approach to analyze sentiment in lengthy Reddit posts from the /r/investing subreddit. Cover topics including the high-level approach, data preparation, initialization, tokenization, chunk preparation, handling CLS and SEP tokens, padding, reshaping for BERT, and making predictions. Gain practical insights into applying transformer models to text of any length for natural language processing tasks.