Python NLTK

A fast tutorial over Python NLTK library. Text analysis use cases and possible profit

1. NLTK Alberts Pumpurs

2. 90 % of world's data generated over last two years

3. common Interne t user creates Visual Textual Instagram Flickr Vscocam Facebook Tumblr Blogger Twitter Facebook Emails Costumer Reviews

4. Detecting hidden signals

5. World is full of unstructured , text-rich data . Everything from emails to customer tweets. The information buried in all that text holds the potential to deliver valuable business insights

6. Text analytics is the practice of using technology to gather, store and mine textual information for hidden signals that can be used to inform smarter business decisions

7. An explosion of unstructured data

8. Many types of organizations are experiencing explosive growth in their unstructured enterprise data . Same time that they have access to external sources of data such as social media, blogs, and mobile data.

9. Until now, much of this information passed through the organization virtually unanalyzed . T oday , new tools for handling large amounts of complex data make s it easier to squeeze value from such unlikely sources .

10. Text Processing use cases

11. sentiment analysis s pam filtering t ext categorization t opic detection keyword frequency plagiatism detection document similarity phrase extraction

12. Natural Language Tool Kit leading platform for building Python programs to work with human language data

13. NLTK Features

14. sentence and word tokenization text calsification corpora parsing clustring part of speach tagging text stemming and mutch more ..

15. Sentence tokenization

16. Word tokenization

17. Part of speech tagging

18. Part of speech tagging explanation CC Coordinating conjunctin CD Cardinal Number DT Determiner EX Existing “ there “ FW Foreign word IN Preposition or subordination conjuction JJ Adjective JJR Adjective- comparative JJS Adjective- superlative LS List item marker MD Modal NN Noun- singular or mass NNS Non-Plural NP Proper noun- singular nltk.help.upenn_tagset () // all tag sets

19. Chunking and NER

20. Text clasification Algorithms in NLTK Naive Bayes Maximum Entropy Decision Tree

21. Text clasification

22. Sentiment analysis https://github.com/pumpurs/SentimentWordsLV/

23. Document similarity detection Tf-idf stands for  term frequency-inverse document frequency , and the tf-idf weight is a weight often used in information retrieval and text mining. This weight is a statistical measure used to evaluate how important a word is to a document in a collection or corpus. 

24. Similarity and concordance

25. Dispersion Plot

26. But where is the

27. “ Market and product reserch ” “ Social CMS ” 1.97 b social network users “ Costumer profiling / analytics ” 70% of marketers used Facebook to gain 6.7 million people blog on blogging sites

28. [email protected] Big Data, Startups , Text Analysis, Internet of Things, Web D evelopment