Natural Language Processing Tutorial Part 2 | NLP Training Videos | Text Analysis

Natural Language Processing Tutorial Part 2 | NLP Training Videos | Text Analysis
Hello and Welcome back to Data Science tutorials powered by Acadgild. In the previous video, we came across the introduction part of the natural language processing (NLP) which includes the hands-on part with tokenization, stemming, lemmatization, etc.
If You have missed the previous video, kindly click the following link for the better understanding and continuation for the series.
In this tutorial, you will be able to learn,
• What are the stop keywords and its importance in the process of text analysis?
Before going to the core topic let’s understand the difference between Lemmatization and Stemming.
Lemmatization Vs Stemming:
Lemmatization:
• Word representations have meaning
• Takes more time than stemming
• Use lemmatization when the meaning of words is important for analysis
• For example, question answering application.
Stemming:
• Word representations may not have any meaning
• Takes less time
• Use stemming when the meaning of words is not important for analysis.
• For example, spam detection
Kindly go through the hands-on part to learn more about the usage of stop keywords in text analysis.
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[venkataraomannem]

Well done it's a good explanation step by step. I am saying form bottom of my heart thank you so much sir. It is easy to understand everyone even who don't no anything. Superb sir.

[ijeffking]

The wonderful step-by-step explanation makes it easy to understand. Thank you so much.

[sharadkant]

why we are using stemming directly we use Lemmatization .i wanted to know why stemming comes into picture