When Using NLP, Which Language Is Best, and Why?

Natural language processing (NLP) represents the intersection between data science and artificial intelligence (AI). Its goal is to progress the development of software and robotics capable of understanding and interpreting human speech. A handful of languages are available for NLP programming, with Python being the most commonly utilised. In this piece, we’ll delve into the Python modules utilised in NLP and explore the reasons that have led to Python’s dominance in this field. We will also briefly outline the other programming languages used in NLP.

Employing Python for Natural Language Processing Yields Several Benefits.

The field of natural language processing (NLP) is currently receiving significant attention, and justifiably so. NLP holds the ability to offer valuable solutions and insights to customers who are constrained by linguistic barriers. Giants like Amazon, Google, and Facebook are investing millions of dollars into NLP technology to power their chatbots, virtual assistants, product search platforms, and other NLP-enabled services.

Previously, the only individuals with extensive knowledge and experience in processing algorithms, linguistics, machine learning, mathematics, and other related areas had access to Natural Language Processing (NLP) projects. However, today developers can leverage the existing NLP tools and environment to save time and effort on text processing. Python, and its related libraries and tools, prove to be an excellent choice for tackling various NLP challenges.

Several advantages of using Python for NLP applications include:

  • Python’s syntax and semantics are straightforward, making it a superb choice for projects.
  • Python developers can benefit from robust support for integrating with other languages and technologies when creating machine learning models.
  • With the vast range of tools and frameworks for natural language processing (NLP) available in Python, developers can effortlessly accomplish tasks like document categorization, part-of-speech (POS) tagging, sentiment analysis, topic modelling, word vectorization, and much more. Utilizing these NLP tools in Python, developers can expediently construct powerful applications that effectively process natural language data.

Top Python Libraries for NLP

NLTK: Resource for Language Analysis and Modification

Designed by Edward Loper and Steven Bird, the Natural Language Toolkit (NLTK) is a powerful Python library intended for a diverse range of natural language processing (NLP) tasks. It is an esteemed resource for NLP in Python, offering a reliable foundation for Python developers involved in NLP and machine learning (ML) development.

Although NLTK is a powerful and flexible library, it may be challenging to implement in Natural Language Processing (NLP) applications. It can become slow and does not efficiently support the accelerated production processes that are necessary. Furthermore, there is a steep learning curve associated with it. Fortunately, Python developers can utilize several resources such as documentation and tutorials to better comprehend the language.


For Python developers who are new to Natural Language Processing (NLP), the TextBlob package is an invaluable tool. Its straightforward and user-friendly interface streamlines the process of mastering essential NLP tasks such as Part-of-Speech tagging, word extraction, sentiment analysis, and more. In summary, TextBlob proves to be an essential resource for Python developers looking to enhance their NLP skills.

For those new to natural language processing in Python, TextBlob is an outstanding option for developing prototypes. However, it is sluggish and insufficient to meet the demands of more business-oriented natural language processing projects.


Developed in Java at Stanford University, CoreNLP is a natural language processing library that is a superb option for Python developers who are beginning their journey in natural language processing. The library, which can support multiple languages such as Python, responds quickly, making it a dependable choice for use in production settings. Additionally, parts of CoreNLP can be utilized in unison with NLTK for improved efficiency.


A sophisticated framework, Gensim can detect semantic similarities between texts by utilizing vector space modelling and topic modelling techniques. Employing incremental algorithms and data streaming, Gensim can efficiently handle large-scale text corpora.

Compared to other programs that are confined to in-memory and batch processing, Gensim holds an advantage in handling large collections of text. Its superior processing speed and memory efficiency are possible due to the implementation of NumPy. Gensim not only possesses advanced features but also advanced vector space modelling capabilities.


spaCy is a relatively new development that offers robust applications and a broader range of word vectors. Its use of Cython as a programming language enables it to deliver the industry’s fastest parsing speeds, resulting in a quick and efficient experience.

As more attention has been directed to areas such as Artificial Intelligence, Machine Learning, and Natural Language Processing, spaCy has become an essential library despite its language support limitations. This provides a positive outlook for the eventual inclusion of additional languages in the future.


PolyGlot is a Python library with powerful analysis capabilities and an impressive range of supported languages that is often overlooked by developers (read more). The library is highly efficient due to its integration with NumPy and shares several similarities with spaCy. In addition, Polyglot follows pipeline principles that simplify the execution of complex commands, and it can communicate with a wide range of languages.

Experts highly regard PolyGlot for its extensive language support and comprehensive analytical capabilities. It is an outstanding choice for projects that do not necessitate a highly precise level of adherence to SpaCy standards.

Here are some of the most intriguing characteristics and statistics of PolyGlot:

  • Transliteration for 69 different languages is available on this website.
  • The library offers context-aware word embeddings in 137 languages.
  • It facilitates a Comparative Morphological Study of 135 languages.
  • The tokenization system supports a total of 165 languages.
  • The system can detect a total of 196 languages.
  • The system can identify parts of speech in 16 languages.
  • Accurate name recognition is possible in 40 different languages.
  • The system can analyze people’s sentiments or emotions in 136 different languages.


For developers seeking to create machine learning models and other processes, Scikit-learn is an incredibly useful Python package due to its wide variety of methods. It also provides several additional features that can be utilized to develop unique characteristics to tackle categorization problems. Scikit-learn’s user-friendly class feature, which makes it simply accessible in Python, is its primary selling point. Moreover, the tool offers extensive documentation to assist programmers in leveraging its full potential.

It’s important to note that scikit-learn’s text processing capabilities don’t include neural networks. Therefore, if you need to process text for machine learning, you should explore other natural language processing (NLP) tools before returning to scikit-learn to develop your models.


Pattern is one of the most widely used and dependable libraries for working with natural language. It simplifies:

  • Tagging at Point-of-sale (POS)
  • Vector space modeling
  • Clustering
  • SVM
  • N-gram analysis
  • Identifying and analyzing emotions or sentiments
  • WordNet

The Pattern framework offers helpful APIs, such as a web crawler and a DOM parser.


AllenNLP is an innovative natural language processing (NLP) technology with substantial potential for both commercial and academic applications. It’s constructed on PyTorch tools and libraries and offers a user-friendly experience for deep learning in NLP. AllenNLP also seamlessly integrates with spaCy for data preparation.

AllenNLP is a priceless asset for individuals who want to design models from the ground up, and those who want to manage and evaluate experiments. It streamlines the entire process from modeling design to conducting experiments that involve multiple components, with the aim of expediting and optimizing the process. Additionally, AllenNLP enables users to carry out essential customer feedback and purpose analyses required for product and service development.


Python’s Vocabulary is an incredibly useful tool for professionals working in the field of natural language processing (NLP). It offers a rich variety of related words, antonyms, pronunciations, and other relevant information for any given term. The information’s value is returned in simple JSON objects, similar to how values are returned in Python dictionaries and lists. Python’s Vocabulary offers many advantages, including its speed and ease of installation, as well as its user-friendly interface.

To optimize natural language processing with Python, the packages discussed in this article can be utilized. However, comparable outcomes can also be obtained by using other programming languages. In this section, we will explore the relevant programming languages and the recommended books for each one of them.

Programming Languages for Natural Language Processing besides Python


Java is the preferred programming language for NLP due to its robustness. It enables exploration of a range of disciplines, such as Android development.

  • Scheduling full-text search results
  • Clustering
  • Extraction
  • Tagging

Java can be executed on any system and efficiently processes data. The two best libraries for natural language processing tasks using Java are outlined below.

Natural Language Processing in Python with Apache OpenNLP

Apache OpenNLP is an open-source Java library that provides an effective, machine-learning based toolset for the analysis of text written in numerous languages. The library is made up of several components that enable the construction of NLP pipelines, streamlining the process for increased efficiency.

  • Analysis of names and recommendation of new ones
  • Tokenizer
  • Document organizer for sorted files
  • Tagging system for point-of-sale
  • Parser
  • Chunker
  • An algorithm capable of identifying complete sentences.

Apache OpenNLP can be utilized for the following:

  • Tokenization
  • Illustration of sentence fragmentation
  • Tagging of point-of-sale (POS)
  • Identification of objects
  • Identification of natural language
  • Chunking
  • Parsing.


C++ and Java are utilized in creating Unstructured Information Management Applications (UIMA), a framework that provides a dependable platform for developing software applications. UIMA was created through a collaboration between IBM, OASIS, and the Apache Software Foundation.

Through the simplification of the analysis engine responsible for recognizing entities, Apache UIMA enables the conversion of unstructured data into structured information. It also offers a broad selection of tools for packaging components as network services.


R is a potent and adaptable programming language commonly employed for statistical analysis, and it has discovered practicality in natural language processing and learning analytics, encouraging its widespread use. Due to its considerable influence in researching big data, R has become an essential tool for data scientists and researchers.

The two primary R libraries for natural language processing are:


The R package ggplot2 is known for its efficiency in data visualisation tasks. By utilizing the “grammar of graphics” methodology for generating visualization, this package enables users to discern and comprehend the correlations between the visual display of data and its attributes.


R and the knitr package can be utilized to create interactive reports. This technique, termed literate programming, enables the incorporation of R code in several markup languages, such as HTML and Markdown, allowing for the production of dynamic research documents.

Due to its extensive library support, clear syntax, and ease of integration with other languages, Python is a highly regarded language for natural language processing (NLP). Python is a great starting point for developers interested in entering the NLP field because it simplifies the learning process and makes it more straightforward.

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