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Lexical Analysis
Syntactic Analysis
Semantic Analysis
Discourse Integration
Pragmatic Analysis
Applications of Natural Language Processing
NLP can be very useful to us in numerous ways, some of the real-life applications of NLP are:
Automatic Text Summarization
Automatic Text Summarization is the process of Document Summary
creating the most meaningful and relevant summary
of voluminous texts from multiple resources.
Google news, Blogspot, Inshorts app and many
other apps dealing with data summarization works
by using Machine learning algorithms that helps in
producing short and relevant data from the scattered
resources, by identifying the important sections in a
huge textual data. There are two different ways of
creating Automatic text summarization:
• Extractive summarization: In this the selected
text, phrases, sentences or sections are picked up
from the scattered resources and joined appropriately to form a concise summary.
• Abstractive Summarization: In this, the summary is created by interpreting the text from multiple resources
using advanced NLP techniques. This new summary may or may not have text, phrases or sentences from the
original documents.
Sentiment and Emotion Analysis
This application of NLP is very significant as it helps business organizations gain insights on consumers and do
a competitive comparison and make necessary adjustments in the business strategy development. The goal of
sentiment analysis is to identify sentiments among several social media posts or even in a post where emotion
is not always explicitly expressed. Companies use NLP to understand what customers think about their products
and services. Sentiment analysis reflects the overall positive, negative or neutral opinion by person and can be
quantified as discrete. In real world sentiment analysis can be considered as customer satisfaction, brand or product
popularity or fashion trends.
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