Top 20 NLP applications in Data science

Understand the top 20 practical applications of Natural language processing(NLP) and how it deals with unstructured data.

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Natural Language processing(NLP) Applications

As part of dealing the unstructured data, understanding text involves many complications and a lot more interesting findings as well when you dig deep into it.

Let me quickly showcase you some of the top 20 applications of Natural language processing(NLP) covering various domains.

1. Sentiment analysis
2. Topic modeling
3. Email classifier
4. Chat bot
5. Search systems
6. Product classification
7. Multi tag classification
8. Text to image
9. Image to text
10. Document classifiers
11. Summarization
12. Resume data extraction
13. Grammar correction
14. Web text
15. Next word prediction
16. Machine translation
17. QA sessions
18. Fake news detection
19. Plagiarisms tool
20. Log analytics

Lets have a brief understanding on these applications on a lighter note.

1. Sentiment Analysis : Predicting the sentiment score of product, movie or any controversial topic is always a challenge as we need to handle lot of text in various formats, languages and more pre-processing data.
   Applications : Sentiment of a Product from reviews, Predicting Movie score from reviews.
2. Topic modeling : It involves picking the main words from the collection of documents and representing as a bag of words. It is an unsupervised learning of data and some of the popular techniques are LDA,LSA and PLSA.
3. Email classifier : Filtering the mails by understanding the subject and content of mail involves text processing techniques. We can see its practical application in day to day life in mailbox.
4. Chat bots : Greeting, requirement understanding and then giving a right solution to the customers is done by chat bots with either partial or without human intervention. It involves processing of text delivered by the customer, where NLP takes its place.
5. Search systems : Getting the most relevant answers in search systems involves text processing. The improvements happening in all search systems is mainly by improving the way they handle the large unstructured data.
6. Product Classification : Based on the description given the user for a particular product, the products are classified.
   Practical application : Online sellers in any e-commerce sites, give description to their product. Based on the description, the classification will be made in the back end.
7. Multi tag classification :The genre tags that appear for movies in Netflix and other OTT platforms involves text processing of description of movie. Same technique applies to classifying the books based on genre tags.
8. Text to Image : Machine can understand the text and gives back a relevant image. This involves processing the input text given and the returns the most relevant image based on image features.
9. Image to text : Extracting the needful information from images involves NLP techniques. One of the practical applications is extracting details from a car number plate incase if it violates traffic rules .
10. Document clustering : Clustering a large volume of documents based on their content requires text mining, which comes under NLP .Web document clustering is one of the best example in this case.
11. Summarization : After reading the document, the take away points are always a key. If your machine can read the document, it can give you a summary based on NLP.
12. Resume Shortlisting : It is always a tedious task which involves understanding job requirement form and picking the suitable candidate profiles. Using NLP, we can make it much easier. Other applications involve, email id and phone numbers extraction from the resume.
13. Grammar correction : When ever you write a mail or blog, you will get suggestion for wrong spellings and statements which don’t have proper grammar in it. It involves text processing of your data using NLP.
14. Web text : NLP algorithms help web developers to extract insights and help developers understand text based content. Analyze URL, Site map, Analyze tweets, LDA(Auto-keyword tags), summarizer are some of the NLP algorithms for web developers.
15. Next word prediction : The latest improvements in email or any content writing is next word prediction, which uses NLP techniques.
16. Machine translation : Automatically converting one natural language into another, preserving the meaning of input and producing the fluent text in the output language. Google translator is one of the well known example.
17. QA sessions : Most of the websites contains QA sessions, which are much required to avoid answering the same thing multiple times. Based on the understanding the question using NLP , a proper answer can be given.
18. Fake news detection : As the information in social media is not trustworthy all the times, NLP plays a major role in detecting the fake news. WhatsApp fake news detection is one such example in the recent days.
19. Plagiarisms tools: It is presenting someone work or idea with or without their consent, by incorporating into your work without their proper acknowledgment. In the growing web world, plagiarism tools are much helpful which use NLP techniques.
20. Log analytics : To get additional insights into the system behavior, NLP is used. Text processing of logs and creating a word cloud helps in understanding the common text patterns.