The exponential growth of volume and variety of data generated today present unprecedented challenges as well as opportunities for organisations. Companies are now relying on technologies like text analysis and Natural Language Processing and text analysis for making sense of such massively collected data.
Text Analysis is a computational analysis that allows for the examination of text items in order to extract machine-readable facts from them. The purpose of text analysis is to create sets of structured data out of heaps of unstructured, heterogeneous documents. The process can be thought of as the “slicing and dicing” of documents into easy-to-manage data pieces.
Using text analysis is often a first step in many data-driven approaches, as the process extracts machine readable facts from large bodies of texts so they can be entered automatically into a database or a spreadsheet for pattern examination. The database or the spreadsheet are then used to analyse the data for trends, to give a natural language summary.
Text analysis is used to optimise a data-driven approach towards managing content. When textual sources are sliced into easy-to-automate data pieces, fact finding begins for processes like decision making, product development, marketing optimisation, business intelligence and more. When turned into data, textual sources provide valuable information like discovering patterns, automatically managing, using and reusing content, and searching beyond keywords.
Natural Language Processing (NLP)
NLP is an area of computer science and artificial intelligence that examines the interactions between computers and human (natural) languages, and a machine’s ability to understand, or mimic the understanding of human language. Examples of NLP applications include Siri and Google Now.
Recent NLP research is increasingly focusing on the use of ‘deep learning,’ which focus on multiple processing layers to learn hierarchical representation of data for optimising results. In the last few years, neural networks based on dense vector representations have been producing superior results on various NLP tasks. This trend is sparked by the success of word embeddings and deep learning methods.
“Text and unstructured data - such as photos - constitute of 90% of data generated today. In addition, with globalisation, more people are speaking two or more languages in their day to day conversations, and hence NLP and text analysis has become the heart and soul of data science today,” says Pankaj Sharma, a Data Science faculty at Sollers College who has undertaken a research study combining NLP and text analysis.
His study sheds a new light on the use of language in Bollywood movies, and could be extrapolated to be representative of how NLP and text analytics display cultural assimilation of two different languages and develop a new linguistic framework. He analysed 14 Hindi movie scripts by breaking down the text, deciphering patterns and drawing conclusions based on the similarities of speech patterns in the research group. The conclusion offered some interesting insights: Hindi movies are filled with Hinglish (the intermingling of Hindi and English) and that nearly 20% of the lingua franca is now English in day to day communication in both these movies and in real life of Indians.
Pankaj believes his research and its findings open up further possibilities to study influence of language, and the role of data science in better understanding the how different societies assimilate communication.
Sollers College is focused on cutting edge research and the latest trends in data science information gathering and instruction. NLP and text analysis are integral part of our Machine Learning with Python and Data Mining with R modules in the Certificate in Data Science. To find out more about Sollers College, our data science faculty and program methodology, reach out to us at 848-299-5900 or email@example.com.