July 2017
Beginner to intermediate
312 pages
7h 27m
English
Content preview from Python Social Media Analytics
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Bigram extraction
Firstly, as in the previous example, we have to extract all the topics (defined as bigrams) from our dataset. We use exactly the functions from the bigram extraction section and then we obtain the following results:
print(frequency) ('fashion week', 64), ('street style', 50), ('automne hiver', 44), ('haute couture', 29), ('printemps 2017', 28), ('style fashion', 22), ('week printemps', 20), ('comment porter', 20), ('hiver 2016', 20), ('summer outfits', 18), ('ons porter', 15), ('tendances mode', 15), ('prêt porter', 15), ('garde robe', 14), ('street looks', 14), ('printemps 2016', 13), ('2016 2017', 13), ('sweat shirt', 13), ('looks fashion', 12), ('pinterest 30', 12), ('porter jean', 12), ('printemps 2014', 12), ('week ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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