What is Natural Language Processing (NLP)

  • Home
  • Insights
  • Article
  • What is Sentiment Analysis

Qualitative Research Service

News & Trends

Recommended Reads

Data Collection

As the data collection methods have extreme influence over the validity of the research outcomes, it is considered as the crucial aspect of the studies

Introduction

Natural Language Processing (NLP) is a subset of Artificial Intelligence (AI) that allows computers to understand, interpret, analyze and create language (specifically, human language) from both text and speech.

Simply, NLP is the method by which machines can read, listen, understand, and respond to human language in a manner that is like the way humans do.[1]

What Natural Language Processing (NLP) Does

NLP is a combination of linguistics, machine learning, and deep learning to process data in natural language.

  • Understand what a text or spoken message means
  • Identify the intent and context of a message
  • Extract relevant information from a message
  • Produce responses like how a person would respond. [2]

Components of Natural Language Processing (NLP)

  • Text Processing: Its features include breaking down text into actionable parts using tokenization, stemming, lemmatization, and part-of-speech tagging.
  • Syntax Analysis: It uses sentence structure and grammar rules to identify the relationship of words to each other.
  • Semantic Analysis: Its main function is interpreting the meaning of words, phrases, and sentences.
  • Pragmatics: It allows systems to interpret the context, intent, and actual use of language based on the way people speak rather than just the letter of the text.[3]

Natural Language Processing (NLP) Methodology

Data Preprocessing

Clean data/text so that it can be analysed.

Feature Extraction

Convert Raw Text into Numbers using TF-IDF or Embedding techniques.

Model Training

Create and Train models to learn from the Raw Text Data.

Language Understanding

Analyse the Raw Text Data for meaning, context, and Semantics.

Output Generation

Create outputs such as summary, prediction, or response.

Task Execution

Use the created Outputs for Real World Use cases: Chat Bot, Recommendation Systems, etc.

Fig 1 Shows the End-to-end Natural Language Processing (NLP) workflow from raw data to intelligent outputs.

Common Natural Language Processing (NLP) Tasks

Text Classification

Classifying written material like emails or paperwork into pre-established categories.

Sentiment Analysis

Identifying feelings (opinion) as either positive, negative, or neutral

Named Entity Recognition

Identifying name/date/location/organization

Machine Translation

Automatically translating text from another language into your own language

Speech Recognition

Converting oral conversations into written format

Text Summarization

Distilling long-form content down to its most important points

Chatbots and Virtual Assistants

Supporting dialogues. [4]

Real-World Applications

  • Chatbots for interactive discussion.
  • Customer care automation for effective handling of questions from customers.
  • Search engines improve information search abilities.
  • Voice assistant programs such as. ALEXA, SIRI and GOOGLE ASSISTANT.
  • Monitor social media to assist in the monitoring of trends and sentiment.
  • Document analytics to pull information from the vast amount of written data.
  • Recommendation engine for generating personalised, individualised

recommendation opportunities.[5]

Let your data think, learn, and adapt through AI & ML innovation from StatsWork.

References

  1. Chowdhary, K. (2020). Natural language processing. Fundamentals of artificial intelligence, 603-649. https://link.springer.com/chapter/10.1007/978-81-322-3972-7_19
  2. King, M. (1996). Evaluating natural language processing systems. Communications of the ACM, 39(1), 73-79. https://dl.acm.org/doi/abs/10.1145/234173.234208
  3. Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023). Natural language processing: state of the art, current trends and challenges. Multimedia tools and applications, 82(3), 3713-3744. https://link.springer.com/article/10.1007/s11042-022-13428-4
  4. Chen, S., Zhang, Y., & Yang, Q. (2024). Multi-task learning in natural language processing: An overview. ACM Computing Surveys, 56(12), 1-32. https://dl.acm.org/doi/full/10.1145/3663363
  5. Hagiwara, M. (2021). Real-world natural language processing: practical applications with deep learning. Simon and Schuster. https://books.google.co.in/books?hl=en&lr=&id=Ye9MEAAAQBAJ&oi=fnd&pg=PR11&dq=Real-