Application of Natural Language Processing Using Python for Text Understanding, Analysis, and Generation

Virtual assistants such as Apple’s Siri, Microsoft’s Cortana, and Amazon’s Alexa are among the most renowned achievements of Natural Language Processing (NLP) research and development. Through these remarkable products, NLP technology has shown its potential in revolutionising the way we interact with machines, beyond being just virtual assistants.

Natural Language Processing (NLP) is a type of Artificial Intelligence (AI) that enables computers to interpret and interact with human language. With NLP, computers can understand and respond to human language in a way that seems natural. NLP has various potential applications that surpass its present use in voice assistants. This technology can be used to examine vast amounts of unstructured text, discover patterns in text, aid automated translation, and create human-like responses to inquiries. As NLP continues to advance, more elaborate applications will become possible, providing new opportunities for businesses to benefit from.

Chatbots are interactive, conversational components commonly located on a website’s homepage, and they represent an example of the ever more sophisticated artificial intelligence technology that is rapidly growing in our society. Their conversational style is so realistic that distinguishing whether we are talking to a human or computer can often be challenging.

The potential applications of Natural Language Processing (NLP) are extensive and frequently prompts the question of how this is achievable. Particularly intriguing is the part played by Python in this operation. To gain a better understanding of the steps needed to generate text in Python, it is necessary to review the information provided in this article. Here, not only will readers find responses to their inquiries concerning the use of Python in NLP, but they will also gain insights into the larger range of possibilities that this technology offers.

How is it possible to create text?

Writing systems, dialects, semantics, and syntaxes differ substantially across the various languages spoken globally. As we become more dependent on online communication, the intricacies of these linguistic components become even more apparent, with the abundance of jargon and acronyms used in day-to-day writing.

Our team has created systems that use machine language and linguistics to understand and respond to human thought processes in a similar way despite the complexities of human languages. The combination of Natural Language Processing (NLP) and Python, along with their corresponding libraries, has made this achievable. Since text is considered unstructured data, Python is crucial for assisting computers in interpreting it.

Generating Text using Python

Text is a type of data that lacks organisation or sequence. It is regarded as “unstructured” data, making it difficult for computers to decipher. Sequential data, on the other hand, is information that occurs in a specific sequence. To enable computers to generate text for humans to comprehend, they must be trained to interpret and construct language using structured input. Once accomplished, machines can present meaningful information for us to analyse.

Below is a summary of the steps required in Python to produce text:

  1. Importing Dependencies
  2. Loading and Mapping Python Data
  3. Analysis of Text
  4. Producing Synthetic Language and Models for Natural Language Processing.

Importing Dependencies is the First Step

When starting a new Python project, the first step is to import all essential components. Vital dependencies include:

  • NLTK:

    The Natural Language Toolkit is a comprehensive suite of libraries and modules used for developing Python applications related to NLP.
  • NumPy:

    A series of programs used for performing computations on data.
  • Pandas:

    Another Python package that can assist with organising data.

This page provides guidelines for importing necessary packages.

Step 2: Importing Data into Python and Mapping it

The second step of the process involves importing data into a Python platform for ease of use and readability. Following that, a map must be created. The computer assigns a numerical value to each item and stores it in a dictionary data structure, thus completing the mapping process. As an example, the word “hello” is represented by the 4-digit binary integer 0011.

Since computers are better at handling numbers than words, mapping is used to enhance the comprehension of machine reading.

Step 3: Analysing the Text for Meaning

Textual analysis refers to extracting meaningful information from unstructured text data. It often involves eliminating unnecessary words or phrases to create a more organised dataset. Words that carry no important meaning are removed, leaving only the significant terms. After that, the data is cleaned, processed, and converted to a more structured format. This structured information is used to draw valuable insights from the text, resulting in more efficient decision-making.

Within this context, the body of text utilised as a training dataset for a system is referred to as a corpus.

The process goes as follows:

  • Data Cleansing:

    This step involves removing capitalisation and punctuation from the processed data.

    An English theoretical physicist, novelist, and cosmologist named Stephen Hawking.

    Stephen Hawking was an English novelist and cosmologist who was also a theoretical physicist.
  • Tokenisation:

    The information is divided into its individual words using the sent_tokenize function. It is a process that can be performed on both individual words and larger bodies of text.

    A well-known cosmologist and novelist hailing from England, Stephen Hawking worked and studied in the field of theoretical physics.

    Stephen Hawking was a
    physicist and author from England
    also a cosmologist
  • Filtering:

    Stop words, which are words with no important content, can be excluded from the text. Examples of stop words include articles (e.g. ‘an’, ‘and’), pronouns (e.g. ‘that’, ‘who’, ‘all’), contractions (e.g. ‘is’, ‘am’, ‘didn’t’), etc.

    Stephen Hawking was a
    physicist and author from England
    as well as a cosmologist

    English language theoretician Stephen Hawking
    author, cosmologist and physicist

    Filtered information is sometimes referred to as a “bag of words,” a term used to describe information presented in textual form. This information can be quickly and easily converted to a matrix.
  • Stemming:

    Stemming involves breaking down words into their constituent parts to identify their root forms. The PorterStemmer algorithm, which is included in the Natural Language Toolkit (NLTK) library, can be used to effectively teach a machine to recognise that words such as “killing” and “kill” have the same root word.
  • Word Annotation: Parts of Speech

    It is evident that all living languages feature a range of different grammatical structures. Parts of Speech (POS) Tagging is a method used to assign a specific value to each of the grammatical categories of a particular piece of text. This is a crucial process that allows us to gain a precise understanding of the structure and meaning of the given text.
  • Lemmatization:

    Computers use the provided data to determine the morphological analysis of each word or lemma. Utilising a dictionary is necessary in performing the analysis required for this process.

Upon completion of a text analysis, the data can be accessed in a more structured format for further use.

The fourth stage involves modelling and generating text.

The construction of a model is the basis for text generation. In the beginning, the computer is trained to produce text through the use of sample input and output data. This enables the computer to comprehend a wide range of linguistic structures in spoken language. Therefore, the computer is capable of automatically generating the output in the future when given the input.

The Long Short-Term Memory (LSTM) model is a crucial element in accurately predicting sequential data. This model differs from its predecessors due to its capacity to store pattern information for extended periods of time, making it invaluable for natural language processing applications such as text-to-voice converters.


Suppose we were developing a model that has four inputs and one output (Text: world).

It is apparent that this pattern remains consistent across all possible input lengths. Once the training is complete, the model is evaluated through an experiment. For example, if the initial input is “worl,” the output would be “d.”

Textual data can be difficult to interpret without undergoing preprocessing. Preprocessing is necessary to ensure that the data is presented in an organised manner. It is essential to note that the efficiency of text production is heavily reliant on models. The text generation process is divided into two stages: practice and evaluation. During the practice stage, techniques are developed to enable computers to recognise patterns in text. In the evaluation stage, new texts are generated based on the models created during the practice stage. Python and the Natural Language Toolkit (NLTK) provide the necessary capabilities to accomplish all of this.

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