The module NLTK can automatically tag speech.
Given a sentence or paragraph, it can label words such as verbs, nouns and so on.
NLTK – speech tagging example
The example below automatically tags words with a corresponding class.
import nltk from nltk.tokenize import PunktSentenceTokenizer document = 'Whether you\'re new to programming or an experienced developer, it\'s easy to learn and use Python.' sentences = nltk.sent_tokenize(document) for sent in sentences: print(nltk.pos_tag(nltk.word_tokenize(sent)))
This will output a tuple for each word:
where the second element of the tuple is the class.
The meanings of these speech codes are shown in the table below:
We can filter this data based on the type of word:
import nltk from nltk.corpus import state_union from nltk.tokenize import PunktSentenceTokenizer document = 'Today the Netherlands celebrates King\'s Day. To honor this tradition, the Dutch embassy in San Francisco invited me to' sentences = nltk.sent_tokenize(document) data =  for sent in sentences: data = data + nltk.pos_tag(nltk.word_tokenize(sent)) for word in data: if 'NNP' in word: print(word)
The classes include past tense, present. Using this technique we can quickly derive meaning from a text.