What is Natural Language Processing NLP?

nlp analysis

Natural language processing is the artificial intelligence-driven process of making human input language decipherable to software. Feel free to click through at your leisure, or jump straight to natural language processing techniques. But how you use natural language processing can dictate the success or failure for your business in the demanding modern market. This NLP technique is used to concisely and briefly summarize a text in a fluent and coherent manner.

What is NLP data analysis?

Natural Language Processing (NLP) is a field of data science and artificial intelligence that studies how computers and languages interact. The goal of NLP is to program a computer to understand human speech as it is spoken.

Specific NLP processes like automatic summarization — analyzing a large volume of text data and producing an executive summary — will be a boon to many industries, including some that may not have been considered “big data industries” until now. There is no qualifying theme there, but the sentence contains important sentiment for a hospitality provider to know. Left alone, an n-gram extraction algorithm will grab any and every n-gram it finds. To avoid unwanted entities and phrases, our n-gram extraction includes a filter system. This is where theme extraction and context determination comes into play.


NLP can be used to automate the process of resume screening, freeing up HR personnel to focus on other tasks. NLP can be used to analyze financial news, reports, and other data to make informed investment decisions. NLP can be used to extract information from electronic medical records, assist with diagnosis, and improve patient outcomes.

However, we still found that terms with similar semantics, i.e., Data mining and Text mining were not clustered into the same cluster in the same context NLP, which might cause confusion. AP clustering was conducted based on the key terms correlation matrix, and the matrix was calculated using Ochiai correlation coefficient. Hence, the clustering results might be vulnerable to choices of both calculation method and clustering method. Therefore, in our future work, we will conduct comparison on different calculation methods of correlation matrix as well as different clustering methods for further exploration. In our study, AP clustering method was performed based on top 50 high frequency key terms in order to acquire a moderate number of categories.

Sentiment Analysis

It is used for extracting structured information from unstructured or semi-structured machine-readable documents. Say there is a sudden increase in negative reviews—your company can quickly identify the cause and take action to address it. Additionally, tracking online reputation over time can help you identify trends and make data-driven decisions. By analyzing online content regularly, businesses can track changes in sentiment and respond appropriately. Some of the key features provided by Natural Language Toolkit’s libraries include sentence detection, POS tagging, and tokenization.

nlp analysis

But in the era of the Internet, where people use slang not the traditional or standard English which cannot be processed by standard natural language processing tools. Ritter (2011) [111] proposed the classification of named entities in tweets because standard NLP tools did not perform well on tweets. They re-built NLP pipeline starting from PoS tagging, then chunking for NER. The goal of NLP is to accommodate one or more specialties of an algorithm or system.


Lexical or Morphological Analysis [newline]Lexical or Morphological Analysis is the initial step in NLP. The collection of words and phrases in a language is referred to as the lexicon. Lexical analysis is the process of breaking down a text file into paragraphs, phrases, and words. The source code is scanned as a stream of characters and converted into intelligible lexemes in this phase.

nlp analysis

One cloud APIs, for instance, will perform optical character recognition while another will convert speech to text. Some, like the basic natural language API, are general tools with plenty of room for experimentation while others are narrowly focused on common tasks like form processing or medical knowledge. The Document AI tool, for instance, is available in versions customized for the banking industry or the procurement team. It offers a basic API for doing standard natural language processing (NLP) activities including part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, and translation, among others.

Statistical analysis

We need to project both the Page and the Keyword nodes along with the connecting HAS_KEYWORD relationship. We need to reverse the relationship orientation in the graph projection as we want to examine clusters of co-occurring keywords and not groups of similar web pages. Having the right keywords on the right web pages is one of the critical aspects of search engine optimization. We can get a high-level overview of the page by examining its most frequent keywords. Many links also point to specific tags of the developer blog and graph gists.

nlp analysis

The main benefit of NLP is that it improves the way humans and computers communicate with each other. The most direct way to manipulate a computer is through code — the computer’s language. By enabling computers to understand human language, interacting with computers becomes much more intuitive for humans. NLP has existed for more than 50 years and has roots in the field of linguistics.

Text and speech processing

It entails developing algorithms and models that enable computers to understand, interpret, and generate human language, both in written and spoken forms. Pragmatic Analysis

The fifth and final phase of NLP is pragmatic analysis. The overall communicative and social content, as well as its impact on interpretation, are the focus of pragmatic analysis. Pragmatic Analysis uses a set of rules that describe cooperative dialogues to help you find the intended result. It covers things like word repetition, who said what to whom, and so on. It comprehends how people communicate with one another, the context in which they converse, and a variety of other factors.

  • The World Health Organization’s Vaccine Confidence Project uses sentiment analysis as part of its research, looking at social media, news, blogs, Wikipedia, and other online platforms.
  • Since the specification language is essentially mathematical and the domain models can be tested and verified, there is little room for ambiguity and far less chance that a requirement error will not be caught.
  • Phone calls to schedule appointments like an oil change or haircut can be automated, as evidenced by this video showing Google Assistant making a hair appointment.
  • The relevant work done in the existing literature with their findings and some of the important applications and projects in NLP are also discussed in the paper.
  • One can either use predefined Word Embeddings (trained on a huge corpus such as Wikipedia) or learn word embeddings from scratch for a custom dataset.
  • Since 2015,[22] the field has thus largely abandoned statistical methods and shifted to neural networks for machine learning.

What are the 4 phases of NLP?

  • Lexical Analysis and Morphological. The first phase of NLP is the Lexical Analysis.
  • Syntactic Analysis (Parsing) Syntactic Analysis is used to check grammar, word arrangements, and shows the relationship among the words.
  • Semantic Analysis.
  • Discourse Integration.
  • Pragmatic Analysis.