Sentiment analysis teaches a computer to read a piece of text and decide how the writer feels about it: positive, negative or neutral. Companies use it to sort customer reviews, track brand reputation on social media and flag unhappy support tickets before they escalate.
The lessons start by running a pretrained model with the Hugging Face pipeline in a few lines of code. Then you build a full project that compares VADER, a rule-based approach, with a RoBERTa transformer on real Amazon reviews.
Watch the lessons in order and code along in Google Colab or a Jupyter notebook. Once your classifier works, wrap it in a small app that takes a sentence or a list of reviews and returns a sentiment label with a confidence score.
The quickest way to run a pretrained model: create a sentiment-analysis pipeline and classify text in a few lines of code, with the tokenizer and model handled for you.
Video 4minBuild a full classifier for Amazon reviews with NLTK's VADER and a Hugging Face RoBERTa model, then compare how the two approaches score the same text.
Video 45min