Recent advances in technology has led to an increase in the amount of data being generated. The data generated is both structured and unstructured with various format such as numeric, text, images, video and audio. To process and get actionable insights from such data we need sophisticated techniques and tools. Data mining is a term that has been widely used in the process of organizing, extracting, analyzing and finding valuable insights from large pools of data that would otherwise have not been found. In this post we are going to introduce ourselves to text analytics and natural language processing, how they differ, how they relate to each other and there application.
Introduction to Text Analytics and Natural Language Processing
Text analytics is the process of organizing, examining and transforming unstructured text data into a structured format for further analysis with the objective of discovering facts, relationships and assertions. In text analytics we only focus on text data and use statistical and machine learning techniques to find actionable insights from such data. Example of text analytics tasks include getting the frequency of words, length of sentence, searching specific words in a document and document classification.
Natural Language Processing is a computational technique that make computers to interact with humans in a natural manner. Whereas text analytics only focuses on textual data natural language processing (NLP) focuses on all facet human communication including text, speech and vision. NLP uses advanced techniques of artificial intelligence, linguistics, machine learning and statistical models to find the underlying/ latent metadata in data. Examples of NLP tasks include part-of-speech (POS) tagging, natural language understanding and recognition, disambiguation, automatic summarization and named entity recognition among other use-cases.
Difference Between Text Analytics and NLP
Sometimes the terms Text Analytics and NLP are used interchangeably but they are different. Below is the major differences between the two terms.
- Text analytics focuses on textual data while NLP encompasses broad human communication aspects such as text, speech and vision.
- Text analysis heavily utilizes statistical methods in finding hidden patterns from textual data such as frequency of words, length of sentence among others while NLP uses advanced artificial intelligence, machine learning and statistical techniques to understand the semantic representations from the data.
- Text analysis has easy and clear performance measures while NLP requires human intervention to measure the accuracy of the NLP systems.
- The goal of text analytics is to extract valuable patterns from data without focusing on the underlying meaning of the data whereas NLP focuses on understanding the underlying semantic meaning of the data.
Application of NLP
NLP has found applications in different industries including media and advertisement, business intelligence, health, finance, manufacturing, health, self driving car, robotics and internet.
- Semantic search
- Sentiment analysis
- Question answering
- Text classification
- Speech recognition
- Machine translation
- Text Summarization
Text Analytics and NLP Libraries
There are numerous libraries and packages both commercial and open source for working with text analytics and NLP methods. Below are the widely used industrial-grade open source NLP and text analytics libraries;
- Natural Language ToolKit. Natural Language ToolKit (NLTK) is one of the widely used Python based NLP platform for computational linguistics. It comes with many features and easy-to-use interfaces. NLTK is open source and has wide community support. It is supported on Windows, Linux and Mac OS X operating systems. For more details visit www.nltk.org
- CoreNLP. It originates from the Stanford group and distributed under GPL license. CoreNLP is capable of processing data in different languages including English, Chinese, Spanish, French and Arabic. It has various APIs supported by modern programming languages. CoreNLP is a production-ready library that is optimized for speed. For more details visit https://stanfordnlp.github.io/CoreNLP/
- TextBlob. TextBlob is a simple and easy-to-use text processing library with API interfaces to NLP tasks. It is an open source library developed in Python. TextBlob provides a simple interface to NLTK library. To get started visit https://textblob.readthedocs.io/en/dev/index.html
- Gensim. It is a powerful topic, vector space modeling and document similarity library. Gensim is an open source and scalable tool for vector space modeling. For more details visit https://radimrehurek.com/gensim/
- SpaCy. SpaCy is a high performant NLP library written in Cython. It integrates well with deep learning libraries such as TensorFlow, PyTorch, scikit-learn and Gensim. SpaCy is optimized to handle large data and is capable of building complex NLP products. It supports over 33 language and 13 statistical models for 8 languages. For more details visit https://spacy.io/
Natural Language Processing is a very broad field that encompasses many disciplines including linguistics, mathematics, statistics, machine learning, artificial intelligence, linguistics and human psychology in trying to close the gap between human and computer communications. Some NLP methodologies are used in text analytics in the analysis of data. However, text analytics is a shallow field that majorly focuses on the analysis of text data using statistical and machine learning models while NLP focuses on all aspects of human communication such as text, speech and vision. NLP and text analytics have been widely applied in different industries for tasks such as text classification, sentiment analysis, semantic search, speech recognition and in complex systems such as robotics and self-driving cars. In the coming series of posts we are going to look at important concepts of NLP.
In this post we have looked at the text analytics and Natural Language Processing. In the next post we will look at important concepts of NLP.