This is the part - 2 of our series "Natural Language Processing". In previous blog we learn all about text analysis using NLP. In this blog we will learn N - Grams, this is second topics of this series so ready to learn with NLP N-Grams.
Before start it first we will again repeat all about general information which we discuss in part - 1 also.
I suggest that you go through part - 1 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 -N Grams
What is N - Grams ?
N - Grams used to text mining, Language detection and natural language processing tasks. It is basically set of word which occurs in fixed widow. It is also used to find title of text. It move one word forward at every time and left one work backside. depends on user selection grams.
To understand it in general way, here we will go through this basic theoretical example.
Let suppose text is:
"He is very lazy boy."
If N=2(bigrams) orN=3(trigrams)
Then generated bigrams(N=2) is:
He is
is very
very lazy
lazy boy
Total number of N - grams in text
Here formula to find total number of N-grams in text file
Total Ngrams = X - ( N- 1 ) #where X is total number of word
Use of N - Grams
There are many uses of N - Grams. like
spelling corrections
word breaking and
text summarization
Here we will start it with advanced level with the help of examples :
Step - 1:
First tokenize text and removing punctuation
Run with jupyter notebook:
Step 2:
Generating 2 - Grams :Frist import this
from nltk.util import ngrams
Run with jupyter notebook:
If you want to select fixed field then used this line of code:
print(generated_2grams[:8])
Step 3:
Short n grams as per frequency
Run with jupyter notebook:
Thanks for reading, part - 2 is finished in next part we will learn "Detecting Text Language"
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