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Natural Language Processing In Python : Part - 3

This is the part - 3 of our series "Natural Language Processing". In previous blog we learn all about text analysis using NLP. In this blog we will learn Detecting text language, this is third topic of this series so ready to learn with NLP Detecting text language.

Before start it first read last parts, part - 1 and part - 2.

I suggest that you go through part - 1 and part - 2 before start it which is also more help-full for this NLP Series.

What is NLP ?

It is the branch of data science that consists of systematic processes for analyzing, understanding, and how to driving information from the text data in a smart and efficient manner.

First install libraries which is related to NLP -

nltk, numpy, matplotlib.pyplot, tweepy, TwitterSearch, unidecode, langdetect, langid, gensim

And then import all of these:

Install these all libraries which use in this

import nltk #

import numpy #

import matplotlib.pyplot #

import tweepy #

import TwitterSearch #

import unidecode #

import langdetect #

import langid #

import gensim

List of Topics which we will covers in this series:

  • Text-analysis using NLTK library

  • N-Grams

  • Detecting text language

  • Language identifier

  • Stemming and Lemmatization using Bigrams

  • Finding unusual words

  • part of speech and meaning

  • Name-Gender identifier

  • Classify document into categories

  • Sentiment Analysis

  • Sentiment Analysis with NLTK

  • Work with Twitter streaming and Cleaning

  • Language detection

Now let's starts Topics -Detecting text language

Detecting text language - work with stop word

Step - 1

As per previous parts first need to tokenize text and than process it.

Jupyter notebook output:

Now importing stopword after installing it -

Jupyter notebook output:

In the last, here final code with count number of stop words in text file -

Jupyter notebook output:

Thanks for reading, in next blog we will learn new NLP topic - Language identifier

Here we add link of last two parts so you can go through it -

Thanks for reading this blog, next part we will covers N-Grams.

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