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The hands-on NLTK tutorial for NLP in Python
| Date | Stars |
|---|---|
| 2026-07-24 | 573 |
| 2026-07-25 | 573 |
| 2026-07-28 | 573 |
| 2026-07-30 | 573 |
| 2026-08-06 | 573 |
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# Hands-On NLTK Tutorial [](https://mybinder.org/v2/gh/hb20007/hands-on-nltk-tutorial/master) > The hands-on NLTK tutorial in the form of Jupyter notebooks NLTK is one of the most popular Python packages for Natural Language Processing (NLP). ## Index of Jupyter Notebooks | Notebooks | | ------------------------------------------------------------------------------------------------------------------ | | [1.1 Downloading and Checking Packages][1.1]<br>*Getting ready to start!* | | [1.2 Text Analysis Using `nltk.text`][1.2]<br>*Extracting interesting data from a given text* | | [2.1 Deriving N-Grams from Text][2.1]<br>*Creating n-grams (for language classification)* | | [2.2 Detecting Text Language by Counting Stop Words][2.2]<br>*A simple way to find out what language a text is in* | | [2.3 Language Identifier Using Word Bigrams][2.3]<br>*State-of-the-art language classifier* | | [3.1 Bigrams, Stemming, and Lemmatizing][3.1]<br>*NLTK makes bigrams, stemming, and lemmatization super easy.* | | [3.2 Finding Unusual Words for a Given Language][3.2]<br>*Which words do not belong with the rest of the text?* | | [3.3 Creating a POS Tagger][3.3]<br>*Creating a parts-of-speech tagger* | | [3.4 Parts of Speech and Meaning][3.4]<br>*Exploring awesome features offered by WordNet* | | [4.1 Name Gender Identifier][4.1]<br>*Building a classifier that guesses the gender of a name* | | [4.2 Text Genre Classifier][4.2]<br>*Building a classifier that guesses the genre of a text* | | [5.1 Sentiment Analysis][5.1]<br>*Is a movie review positive or negative?* | | [5.2 Sentiment Analysis with `nltk.sentiment.SentimentAnalyzer` and VADER][5.2]<br>*More sentiment analysis!* | | [6.1 The `langdetect` and `langid` Libraries][6.1]<br>*Useful libraries for language identification* | | [6.2 NLTK with the Greek Script][6.2]<br>*Using NLTK with foreign scripts* | [1.1]: 1-1-Downloading-and-Checking-Packages.ipynb [1.2]: 1-2-Text-Analysis-Using-nltk.text.ipynb [2.1]: 2-1-Deriving-N-Grams-from-Text.ipynb [2.2]: 2-2-Detecting-Text-Language-by-Counting-Stop-Words.ipynb [2.3]: 2-3-Language-Identifier-Using-Word-Bigrams.ipynb [3.1]: 3-1-Bigrams-Stemming-and-Lemmatizing.ipynb [3.2]: 3-2-Finding-Unusual-Words-for-a-Given-Language.ipynb [3.3]: 3-3-Creating-a-POS-Tagger.ipynb [3.4]: 3-4-Parts-of-Speech-and-Meaning.ipynb [4.1]: 4-1-Name-Gender-Identifier.ipynb [4.2]: 4-2-Text-Genre-Classifier.ipynb [5.1]: 5-1-Sentiment-Analysis.ipynb [5.2]: 5-2-Sentiment-Analysis-with-nltk.sentiment.SentimentAnalyzer-and-VADER.ipynb [6.1]: 6-1-The-langdetect-and-langid-Libraries.ipynb [6.2]: 6-2-NLTK-with-the-Greek-Script.ipynb
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matched fp:484073c7257374be, topic:tutorial, name:tutorial, desc:tutorial
matched fp:484073c7257374be, topic:nlp, readme:natural language processing, readme:sentiment analysis