Top 20 NLP applications in Data Science

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 modelling
  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
  1. Summarization
  2. Resume data extraction
  3. Grammar correction
  4. Web text
  5. Next word prediction
  6. Machine translation
  7. QA sessions
  8. Fake news detection
  9. Plagiarism tool
  10. Log analytics

Let’s 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 modelling

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 in case 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

Whenever 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. Analyse URL, Site map, Analyse 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. Plagiarism 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 behaviour, NLP is used. Text processing of logs and creating a word cloud helps in understanding the common text patterns.


Rakesh Maddipati

CAE Engineers, Mando Crop.

MTech in AI, Class of 2022

Leave a Reply

Your email address will not be published. Required fields are marked *