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| == NPL with NLTK/Python ==
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| *Count number of words (word tokens) : len(text)
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| *Count number of distinct words (word types) : len(set(text))
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| *The diversity of a text can be found with : len(text) / len(set(text))
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| *Dispersion plot : shows you usage of certain words in time (useful for quick overviews) (i.e. text.dispersion_plot(['of','the']))
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| *Collocations : 2 words that are almost always together (i.e. red wine) text.collocations()
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| *Join/split to create strings/lists from delimiters
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| *All the words starting with B in text 5. Sorted and unique words only : sorted([w for w in set(text5) if w.startswith('b')])
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| *Find exact occurrence of a word = text.index('word')
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| *Find all 4-letter words in a text :
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| <source lang='python'>
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| V = set(text8)
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| fourletter = [w for w in V if len(w)==4]
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| sorted(fourletter)
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| </source>
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| *And show their distribution in order
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| <source lang='python'>
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| fdist = FreqDist(text5)
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| vocab = fdist.keys()
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| for w in vocab:
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| if len(w)==4:
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| print w
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| </source>
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| *Find all words containing 'ma' in them, sorted.
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| <source lang='python'>
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| res = sorted([w for w in set(text) if 'ma' in w])
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| </source>
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| *How often a given word occurs in a text, expressed as a percentage
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| <source lang='python'>
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| fdist = FreqDist(text)
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| fdist['word']/len(text)
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| </source>
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