Part-of-speech Tagging using TextBlob in Python
In this article, we’ll learn about Part-of-Speech (POS) Tagging in Python using TextBlob.
POS Tagging or Grammatical tagging assigns part of speech to the words in a text (corpus). This means that each word of the text is labeled with a tag that can either be a noun, adjective, preposition or more.
We’ll use textblob library for implementing POS Tagging. So, install textblob using the given command below –
pip install textblob
POS Tags in Python
These are some of the POS Tags mentioned below –
C: conjunction, coordinating CD: numeral, cardinal DT: determiner IN: preposition or conjunction, subordinating JJ: adjective or numeral, ordinal NNP: noun, proper, singular
If you want to learn about more of these tags, follow the steps below –
- Install NLTK library using the command given below –
pip install nltk
- Import NLTK library
import nltk
- Enter this command to download required NLTK data –
nltk.download('tagsets')
- Enter the following command for the POS tags list –
nltk.help.upenn_tagset()
Now let’s implement POS tags using the TextBlob library through an example.
Example of part-of-speech tagging in Python programming
from textblob import TextBlob text = ("Codespeedy is a programming blog. " "Blog posts contain articles and tutorials on Python, CSS and even much more") tb = TextBlob(text) print(tb.tags)
- Import textblob library using import keyword.
- Create a TextBlob object tb. This tokenizes all the words of the text which will then be passed onto the tag attribute.
- The tag attribute assigns each word with the respective POS tag. This will give an output in the form of (word, tag).
This gives the following output –
[('Codespeedy', 'NNP'), ('is', 'VBZ'), ('a', 'DT'), ('programming', 'VBG'), ('blog', 'NN'), ('Blog', 'NNP'), ('posts', 'NNS'), ('contain', 'VBP'), ('articles', 'NNS'), ('and', 'CC'), ('tutorials', 'NNS'), ('on', 'IN'), ('Python', 'NNP'), ('CSS', 'NNP'), ('and', 'CC'), ('even', 'RB'), ('much', 'RB'), ('more', 'JJR')]
I hope you all liked the article!
Also read-
Introduction to Natural Language Processing- NLP
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