Text analytics applications must utilize a variety of technologies to provide effective and easy-to-use solutions. Natural Language Processing (NLP) is one such technology and is essential for creating applications that combine computer science, artificial intelligence (AI), and linguistics. However, to implement NLP algorithms, you must use compatible programming languages.
This article describes how to use NLP tools (including available libraries) for text analytics applications in Python and how to use them.
Purpose of natural language processing
NLP is a type of artificial intelligence that can understand the semantics and implications of human language while effectively identifying usable information. This captured information (and the collected insights) can be used to build effective data models for a variety of purposes.
When it comes to text analysis, NLP algorithms can perform various functions such as:
- text mining
- text analysis
- text classification
- voice recognition
- voice generation
- sentiment analysis
- word ordering
- machine translation
- Create a dialogue system
- more
This feature puts NLP at the forefront of deep learning environments, allowing us to extract important information with minimal user input. This will greatly improve technologies such as chatbots, while facilitating the development of tools ranging from image content querying to speech recognition.
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Natural Language Processing and Python Libraries
A high-level, general-purpose programming language, Python can be applied to NLP to deliver a variety of products, including text analytics applications. This is thanks to Python’s many libraries built specifically for NLP.
A Python library is a group of related modules that contain bundles of code that can be reused in new projects. These libraries make life much easier for developers by not having to rewrite the same code over and over again.
Python’s NLP library aims to make text preprocessing as easy as possible. This allows applications to accurately transform free text sentences into structured features that can be used in machine learning (ML) or deep learning (DL) pipelines. Combined with our user-friendly API, you can quickly and easily implement the latest algorithms and NLP models so you can continue to grow and improve your applications.
Top 5 Python NLP Libraries
Now that you know what you can do with natural language processing and what the Python NLP library is for, let’s take a look at some of the best options available today.
1. Text Blob
TextBlob is a Python (2 and 3) library used for processing text data, primarily focused on providing access to common text processing functions through an easy-to-use interface. Objects in a TextBlob are available as Python strings that can provide NLP functionality to help build text analytics applications.
TextBlob’s API is very intuitive and makes it easy to perform various NLP tasks such as noun phrase extraction, language translation, part-of-speech tagging, sentiment analysis, and WordNet integration.
This library is highly recommended for those relatively new to developing text analysis applications. Because text can be processed in a few lines of code.
2. Spacey
This open-source Python NLP library has established itself as the go-to library for production use, simplifying the development of applications focused on processing large amounts of text in a short amount of time. increase.
SpaCy can be used to preprocess text in deep learning environments, build systems that understand natural language, and create information extraction systems.
Two of SpaCy’s main selling points are that it has many pre-trained statistical models and word vectors, and it supports tokenization for 49 languages. SpaCy is also preferred by many Python developers for its extreme speed, analysis efficiency, deep learning integration, convolutional neural network modeling, and named entity recognition capabilities.
3. Natural Language Toolkit (NLTK)
NLTK consists of a wide range of text processing libraries and is one of the most popular Python platforms for processing human language data and text analytics. Preferred by experienced NLP developers and novices, this toolkit provides an easy introduction to programming applications designed for language processing.
Some of the key features provided by the Natural Language Toolkit libraries include sentence detection, POS tagging, and tokenization. For example, tokenization is used in NLP to break paragraphs and sentences into smaller components and assign them specific, more comprehensible meanings.
NLTK has a very simple interface with over 50 corpora and vocabulary resources. Thanks to the large number of libraries available, NLTK provides all the essential functionality to complete almost any type of his NLP task within Python.
4. Genism
Genism is a bespoke Python library designed to provide document indexing, topic modeling, and search solutions using large numbers of corpus resources. The algorithms within Genism are memory bound in terms of corpus size. This means that the system can handle inputs that exceed the available RAM.
Hierarchical Dirichlet Process (HDP), Latent Dirichlet Assignment (LDA), Latent Semantic Analysis (LSA/LSI/SVD), and Random Projection (RP).
Genism’s accessibility is further enhanced by the large amount of documentation available in addition to the Jupyter Notebook tutorial. Note, however, that using Genism also requires the Python packages SciPy and NumPy to be installed for its scientific computing capabilities.
5. PyNLPl
Last on the list is PyNLPl (Pineapple). This is a Python library consisting of several custom Python modules designed specifically for NLP tasks. PyNLPl’s most notable feature is its comprehensive library for developing Format for Linguistic Annotation (FoLiA) XML.
The platform is split into different packages and modules that allow for both basic and advanced tasks, from extractions such as n-grams to more complex functions. This makes it a great choice for NLP developers regardless of experience level.
Conclusion
Python is the programming language of choice for developing text analytics applications because of its rich set of custom libraries focused on providing natural language processing capabilities.
Five of the best NLP libraries available are TextBlob, SpaCy, NLTK, Genism, and PyNLPl. It is based on accessibility, intuitive interface and range of features.
