Page 283 - AI Ver 1.0 Class 10
P. 283

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.



                                                                               Natural Language Processing  281
   278   279   280   281   282   283   284   285   286   287   288