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https://github.com/hb20007/hands-on-nltk-tutorial

The hands-on NLTK tutorial for NLP in Python
https://github.com/hb20007/hands-on-nltk-tutorial

binder didactic jupyter jupyter-notebook jupyter-notebooks nlp nlp-machine-learning nlp-resources nltk nltk-library nltk3 notebook notebook-jupyter notebooks tutorial tutorials

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The hands-on NLTK tutorial for NLP in Python

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# Hands-On NLTK Tutorial



> 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 Libs and Testing That They Are Working](1-1-Downloading-Libs-and-Testing-That-They-Are-Working.ipynb)
*Getting ready to start!*|

|[1.2 Text Analysis Using nltk.text](1-2-Text-Analysis-Using-nltk.text.ipynb)
*Extracting interesting data from a given text*|

|[2.1 Deriving N-Grams from Text](2-1-Deriving-N-Grams-from-Text.ipynb)
*Creating n-grams (for language classification)*|

|[2.2 Detecting Text Language by Counting Stop Words.ipynb](2-2-Detecting-Text-Language-by-Counting-Stop-Words.ipynb)
*A simple way to find out what language a text is written in*|

|[2.3 Language Identifier Using Word Bigrams](2-3-Language-Identifier-Using-Word-Bigrams.ipynb)
*State-of-the-art language classifier*|

|[3.1 Bigrams, Stemming and Lemmatizing](3-1-Bigrams-Stemming-and-Lemmatizing.ipynb)
*NLTK makes bigrams, stemming and lemmatization super-easy*|

|[3.2 Finding Unusual Words in Given Language](3-2-Finding-Unusual-Words-in-Given-Language.ipynb)
*Which words do not belong with the rest of the text?*|

|[3.3 Creating a POS Tagger](3-3-Creating-a-POS-Tagger.ipynb)
*Creating a Parts Of Speech tagger*|

|[3.4 Parts of Speech and Meaning](3-4-Parts-of-Speech-and-Meaning.ipynb)
*Exploring awesome features offered by WordNet*|

|[4.1 Name Gender Identifier](4-1-Name-Gender-Identifier.ipynb)
*Building a classifier that guesses the gender of a name*|

|[4.2 Classifying News Documents into Categories](4-2-Classifying-News-Documents-into-Categories.ipynb)
*Building a classifier that guesses the category of a news item*|

|[5.1 Sentiment Analysis](5-1-Sentiment-Analysis.ipynb)
*Is a movie review positive or negative?*|

|[5.2 Sentiment Analysis with nltk.sentiment.SentimentAnalyzer and VADER tools](5-2-Sentiment-Analysis-with-nltk.sentiment.SentimentAnalyzer-and-VADER-tools.ipynb)
*More sentiment analysis!*|

|[6.1 Twitter Stream (and Cleaning Tweets)](6-1-Twitter-Stream-and-Cleaning-Tweets.ipynb)
*Live-stream tweets from Twitter*|

|[6.2 Twitter Search](6-2-Twitter-Search.ipynb)
*Search through past tweets*|

|[7.1 NLTK with the Greek Script](7-1-NLTK-with-the-Greek-Script.ipynb)
*Using NLTK with foreign scripts*|

|[8.1 The langdetect and langid Libraries](8-1-The-langdetect-and-langid-Libraries.ipynb)
*Useful libraries for language identification*|

|[8.2 Word2Vec (gensim)](8-2-Word2vec-(gensim).ipynb)
*Google's Word2vec*|



## Meta



H. Z. Sababa — hb20007 — 



Distributed under the MIT license. See [`LICENSE`](LICENSE) for more information.