Navigating the World of NLP and Text Analytics

on YouTube ↗01h 38m

" summary

Walkthrough of practical NLP workflows that actually run end to end on Google Colab using NLTK and TextBlob plus spaCy and Gensim, showing regex cleaning, tokenization, stopword removal, stemming vs lemmatization with WordNet, bag‑of‑words and TF‑IDF features, and sentiment scoring with VADER SentimentIntensityAnalyzer. Demonstration uses the Kaggle Quora insincere dataset (subsampled ~100k, split 70k/30k), fits a CountVectorizer + LogisticRegression classifier, reports ~95% accuracy but only ~45% F1 due to 93%/7% class imbalance, and shows where preprocessing helps.

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" chapters (15)

1. 0:00NLP and text analytics intro
2. 1:07Natural language basics
3. 6:04Regex in Python for NLP
4. 10:26Tokenization types
5. 15:49Tokenizing with NLTK
6. 26:15Stemming and lemmatization
7. 32:23Stop words removal
8. 40:15POS tagging with NLTK
9. 44:33Bag of words and word counts
10. 46:38TF-IDF vectorization
11. 50:15Sentiment analysis with NLTK
12. 1:03:00NLP Kaggle use case
13. 1:24:57Text classification with BoW
14. 1:34:29Car review sentiment
15. 1:37:12Q&A

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