Top 5 NLP Tools in Python for Text Analysis Applications
Text analysis applications need to utilize a range of technologies to provide an effective and user-friendly solution. Natural Language Processing (NLP) is one such technology and it is vital for creating applications that combine computer science, artificial intelligence (AI), and linguistics. However, for NLP algorithms to be implemented, there needs to be a compatible programming language used.
In this article, we will discuss using NLP tools in Python for text analysis applications — including available libraries, and how they can be used.
The Purpose of Natural Language Processing
NLP is a type of artificial intelligence that can understand the semantics and connotations of human languages, while effectively identifying any usable information. This acquired information — and any insights gathered — can then be used to build effective data models for a range of purposes.
In terms of text analysis, NLP algorithms can perform a range of functions that include:
- Text mining
- Text analysis
- Text classification
- Speech recognition
- Speech generation
- Sentiment analysis
- Word Sequencing
- Machine translation
- Creating dialog systems
- and more
This functionality has put NLP at the forefront of deep learning environments, allowing important information to be extracted with minimal user input. This allows technology such as chatbots to be greatly improved, while also helping to develop a range of other tools, from image content queries to voice recognition.
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Natural Language Processing and Python Libraries
Python, a high-level, general-purpose programming language, can be applied to NLP to deliver various products, including text analysis applications. This is thanks to Python’s many libraries that have been built specifically for NLP.
Python libraries are a group of related modules, containing bundles of codes that can be repurposed for new projects. These libraries make the life of a developer much easier, as it saves them from rewriting the same code time and time again.
Python’s NLP libraries aim to make text preprocessing as effortless as possible, so that applications can accurately convert free text sentences into a structured feature that can be used by a machine learning (ML) or deep learning (DL) pipeline. Combined with a user-friendly API, the latest algorithms and NLP models can be implemented quickly and easily, so that applications can continue to grow and improve.
The Top 5 Python NLP Libraries
Now that we have an understanding of what natural language processing can achieve and the purpose of Python NLP libraries, let’s take a look at some of the best options that are currently available.
1. TextBlob
TextBlob is a Python (2 and 3) library that is used to process textual data, with a primary focus on making common text-processing functions accessible via easy-to-use interfaces. Objects within TextBlob can be used as Python strings that can deliver NLP functionality to help build text analysis applications.
TextBlob’s API is extremely intuitive and makes it easy to perform an array of NLP tasks, such as noun phrase extraction, language translation, part-of-speech tagging, sentiment analysis, WordNet integration, and more.
This library is highly recommended for anyone relatively new to developing text analysis applications, as text can be processed with just a few lines of code.
2. SpaCy
This open source Python NLP library has established itself as the go-to library for production usage, simplifying the development of applications that focus on processing significant volumes of text in a short space of time.
SpaCy can be used for the preprocessing of text in deep learning environments, building systems that understand natural language and for the creation of information extraction systems.
Two of the key selling points of SpaCy are that it features many pre-trained statistical models and word vectors, and has tokenization support for 49 languages. SpaCy is also preferred by many Python developers for its extremely high speeds, parsing 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 analysis. Favored by experienced NLP developers and beginners, this toolkit provides a simple introduction to programming applications that are designed for language processing purposes.
Some of the key features provided by Natural Language Toolkit’s libraries include sentence detection, POS tagging, and tokenization. Tokenization, for example, is used in NLP to split paragraphs and sentences into smaller components that can be assigned specific, more understandable, meanings.
NLTK’s interface is very simple, with over 50 corpora and lexical resources. Thanks to a large number of libraries made available, NLTK offers all the crucial functionality to complete almost any type of NLP task within Python.
4. Genism
Genism is a bespoke Python library that has been designed to deliver document indexing, topic modeling and retrieval solutions, using a large number of Corpora resources. Algorithms within Genism depend on memory, concerning the Corpus size. This means it can process an input that exceeds the available RAM on a system.
All the popular NLP algorithms can be implemented via the library’s user-friendly interfaces, including algorithms such as Hierarchical Dirichlet Process (HDP), Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA/LSI/SVD), and Random Projections (RP).
Genism’s accessibility is further enhanced by the plethora of documentation available, in addition to Jupyter Notebook tutorials. However, it should be noted that to use Genism, the Python packages SciPy and NumPy must also be installed for scientific computing functionality.
5. PyNLPl
Last on our list is PyNLPl (Pineapple), a Python library that is made of several custom Python modules designed specifically for NLP tasks. The most notable feature of PyNLPl is its comprehensive library for developing Format for Linguistic Annotation (FoLiA) XML.
The platform is segmented into different packages and modules that are capable of both basic and advanced tasks, from the extraction of things like n-grams to much more complex functions. This makes it a great option for any NLP developer, regardless of their experience level.
Conclusion
Python is the perfect programming language for developing text analysis applications, due to the abundance of custom libraries available that are focused on delivering natural language processing functions.
Five of the best NLP libraries available are TextBlob, SpaCy, NLTK, Genism, and PyNLPl. This is based on their accessibility, intuitive interfaces, and range of functionality.
