Top 10 Typical Applications of NLP (Natural Language Preprocessing) – Real World Apps

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In this article, we will discuss the various Typical Applications of NLP. The applications cover almost every aspect of daily life.

Top 10 Typical Applications of NLP (Natural Language Preprocessing)

Why need to Process Text Data?

  • Around 90% of the world’s data is unstructured and may be present in the form of text, image, , audio, and video form
  • Text can come in a variety of forms
  • individual words,
  • sentences to multiple paragraphs
  • form of web, HTML, documents
  • a lot of noise
  • Preprocessing involves transforming raw data into an understandable format.

Typical Application of NLP

• identify parts of speech,
• topic modeling,
• text summarization,
• text generation,
• sentiment analysis,
• and many more applications of NLP
• advanced preprocessing methods,
• POS tagging,
• text similarity,
• text summarization,
• sentiment analysis,
• topic modeling,
• word2vec, seq2seq,

Exploring and Processing Text Data NLP

  • Lowercasing
  • Punctuation removal
  • Stop words removal
  • Text standardization
  • Spelling correction
  • Tokenization
  • Stemming
  • Lemmatization
  • Exploratory data analysis
  • End-to-end processing pipeline

Text Data Processing Frameworks

  • There are dedicated libraries and frameworks for NLP (natural language processing) and text analytics, which you can just install and start using, just like any other built-in module in the Python standard library.
  • These frameworks and libraries have been built over a long period of time and are usually still in active development.
  • The way to assess a framework and libraries is to see how active their developer community is.
  • Each framework contains various methods, and features for operating on text, capabilities, getting insights, and making the data ready for further analysis of data, like applying machine learning algorithms on preprocessed textual data.
  • The following list of frameworks libraries are some of the most helpful text analytics frameworks

Converting Text Data to Lowercase

  • Text wrangling is a process that consists of main steps to clean and standardize textual data into a form that could be consumed by other NLP (natural language processing)  and intelligent systems powered by ML (machine learning) and deep learning.
  • In NLP the key idea is to remove not needed content from one or more text documents in a corpus and get clean text documents.
  • A small case of the text data into all the data in a uniform format
  • By using the default lower()

Removing Punctuation

To remove the repetition of punctuations is very helpful because it does not hold any necessary information if we keep more than one punctuation in the word, for example, raw and fact Need to convert to data.

How to remove stop words

  • Stop words are very common words that take  no meaning,If we remove the words that are less commonly used
  • we can focus on the important keywords
  • for example, if your search query is “How to develop a chatbot using python,”
  • how,” “to,” “create,” “chatbot,” “using,” and “python Programming,” So many Pages can be in the search, but what is our real interest?
  •  remove more common words and rare words

Standardizing Text

  • Most of the text data is in the form of either customer reviews, tweets, or blogs.
  • high chance of people using short words of searching the web.
  • abbreviations to represent the same meaning of the word.
  • For Example msg as for message, and sys as a system.
  • help the downstream process to easy to understand and resolve the semantics of the text.

Correcting Spelling

  • People use short words and make type bugs.
  • This will help us in reducing multiple copies of words that represent the same meaning of the word.
    • For example, “processing” and “processing”
  • These is treated as different words even if they are used in the same sense
  • Note that abbreviations should be handled before this step

Tokenizing Text

  • The process of breaking down or being divided into parts of textual data into smaller and more meaningful components called tokens.
  • There is a sentence tokenizer
  • Word tokenizer
  • There are many libraries to perform tokenization like SpaCy, NLTK, and TextBlob


  • The NLTK (natural language toolkit) package has several implementations for stemmers. These stemmers are implemented into the stem module
  • One of the most popular stemmers is the Porter stemmer, which is based on the algorithm developed by its inventor, Martin Porter
  • The algorithm is said to have a total of five different phases for the reduction of inflections to their stems, each phase has its own set of rules.


A lemmatization is a text normalization technique used in NLP (Natural Language Processing) that switches any kind of a word to its base root of mode. For example, walk, walking, and walk are all forms of the word walk, therefore run is the lemma of all these words

Explanatory Data analysis

Explanatory Data analysis is a step beyond exploratory. Exploratory Data Analysis refers to the critical process of performing start investigations on data so as to discover finding best patterns, spot anomalies, test hypotheses, and to check assumptions with the help of graphical representations of data and summary statistics.

Also Read: Data Science vs Artificial Intelligence vs Machine Intelligence, Which is Better?

End-to-end pip line

NLP Pipeline is a set of many steps followed to build end-to-end NLP software. we started we have to remember this things pipeline is not universal, Deep Learning and machine learning Pipelines are slightly different, and Pipeline is non-linear.


NLP (natural language preprocessing) based on ML (Machine Learning) can be used to establish communication channels between humans and machines. NLP (natural language preprocessing) is important because it helps resolve inexactness in language and adds useful numeric structure to the raw fact and figures for many downstream applications, such as text analytics or speech recognition. The goal of natural language processing (NLP) is to design and build devices that are able to analyze natural languages like English or German and that generate their outputs in a natural language. Typical applications of NLP are information retrieval, text classification, language understanding

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