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 # https://www.nltk.org/install.html
import numpy # https://www.scipy.org/install.html
import matplotlib.pyplot # https://matplotlib.org/downloads.html
import tweepy # https://github.com/tweepy/tweepy
import TwitterSearch # https://github.com/ckoepp/TwitterSearch
import unidecode # https://pypi.python.org/pypi/Unidecode
import langdetect # https://pypi.python.org/pypi/langdetect
import langid # https://github.com/saffsd/langid.py
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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