Natural Language Processing In Web Applications

What is Natural Language Processing (NLP) ?

Natural Language Processing is one powerful application which enables a computer to interpret, understand, and respond to human language in a contextually relevant and meaningful manner. As usage of web and web applications is on a boom, integration of Natural Language Processing has become significantly more important to improve the user experience.

Natural Language Processing in Web and Web Application thrives to improve user experience by providing more customized, efficient, and interesting interactions. By using advanced Machine Learning and Deep Learning techniques, Natural Learning Processing allows web and web applications to understand, analyze and comprehend human language inputs which in turn upgrades these web and web applications to more engaging and user-friendly interfaces.

There are various techniques and algorithms in NLP:

  1. Tokenization 
  2. Part-of-Speech Tagging 
  3. Parsing 
  4. Sentiment Analysis 
  5. Text Summarization 

Each of these techniques has their unique features and it helps in analyzing and understanding user texts better to give more relevant and suitable replies.

Applications of NLP in Web and Web Applications:

  1. Chatbots and Virtual Assistants: Natural Language Powered chatbots and virtual assistants will help in improving more user interface and user experience as they are capable of doing natural and human-like conversations with users. They are capable of doing several tasks, under and interpret user queries and in turn give them relevant responses.
  2. Speech Recognition and Synthesis: Speech Recognition and Speech synthesis are one of the applications offered by NLP. Speech Recognition is the process of converting speech to text and where Speech Synthesis is the process of converting text to speech. These functionalities improve accessibility and hands-free user experience.
  3. Sentiment Analysis: By reading or interpreting various sentiments expressed by the user in reviews, comments and media posts, NLP will provide a detailed outline on how to increase and gain high business value and insights into customers sentiments, preferences, and satisfaction levels.
  4. Keyword Extraction: Certain relevant keywords from the texts of the users will be extracted and the NLP algorithm will identify them, which will be used for search engine optimization, user-centric recommendations based on user interests and content categorization.
  5. Content Categorization: In web pages which are the primary interface for huge amounts of content, it will be hard for users to find his relevant content. So, NLP can automatically interpret content and content matter and it will be able to categorize and organize the content based on the context and themes.
  6. Automatic Summarization: There has been a significant increase in data on the web, with this much data available it is difficult to process. This increase has drastically expanded the scope of processing the data. NLP has a solution to that, it can summarize the whole data and its meaning, and it can also the emotional meaning hidden inside the information. This makes the summarization process quick and efficient.
  7. Language Translation: Translation services based on NLP translation gives power to users to seamlessly communicate in multiple and different languages as it provides real-time translation on social media applications, web applications and other websites which are available for a wide range of global users.

For Web Applications to incorporate NLP into Projects, there are various tools and libraries available:

  1. TensorFlow: This tool is a machine learning library developed by Google. This is used widely for Natural Language Processing tasks due to its high flexibility, functionality, and the way it assists deep learning.
  2. NLTK (Natural Language Processing Toolkit): This is a Python library for Natural Language Processing which is comprehensive and resourceful. It has an easy to understand and use interface and it provides interfaces for over 50+ corpora and lex resources, as well as many features which help in text processing, tokenization, and classification.
  3. PyTorch: This is developed by the AI research lab at Facebook, this is also another open-source machine learning library which supports Natural Language Processing tasks. This makes it easier for developers to debug, experiment and interact with their models and it is popularly known for its dynamic computation graph.

There are other popular tools and libraries such as spaCy, Dialog Flow, Microsoft LUIS, IBM Watson.

By using these tools and libraries in Web Development; Web and Web Application services can easily use Natural Language Processing Features in the Web and Web Applications.

Some challenges associated with NLP:

  1. Addressing Language Ambiguity: Human Language is vastly diverse and understanding it might be a little hard as there is a huge ambiguity that can be expressed through different languages. To understand this ambiguity NLP often needs to understand the messages through contextual knowledge. 
  2. Scaling NLP Applications: As Web and Web Applications cater to high and large numbers of users across the globe, ensuring that the NLP will be handling these large datasets efficiently and accurately is highly crucial. To get this there must be high optimizations and resource utilization should be there to provide constant responsiveness and accuracy.

Apart from these challenges there are other challenges such as multilingual support, improving NLP models with limited data, and ensuring privacy and security.


The integration and usage of NLP in web and web applications has enormous potential to optimize and improve user experience. As NLP is constantly advancing there might be a future where integration of NLP with Web will show groundbreaking advancements.



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Article By, Dinesh Sai Kumar Pilla

Reviewed By, Meghana Prabhandham