An Introduction to Sentiment Analysis of...
summary
Shows a step‑by‑step claim that one can build an SA model from scratch with open‑source Python, specifically NLTK and spaCy alongside scikit‑learn; covers tokenization, stop‑word removal, stemming, CountVectorizer/tf‑idf, fitting a classifier (random forest) and held‑out evaluation. Applies that pipeline to customer review data (an Amazon Alexa subset), provides working code and basic accuracy numbers, and flags installation and preprocessing pitfalls; result is a runnable beginner classifier, not a production‑grade transformer model.
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chapters (10)
- 0:00Introduction to sentiment analysis
- 6:27What is sentiment analysis
- 12:13NLP and sentiment terms
- 18:15Python setup and notebooks
- 21:54Pre-processing text with NLTK
- 33:31Tokenization and stemming
- 53:01Exercise one dataset
- 1:07:33Vectorizing text with scikit-learn
- 1:17:21Training sentiment classifier
- 1:28:06Q&A
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