How NLP is Automating the complete Text Analysis Process for Enterprises?
In a world where we generate 2.5 quintillions (number of zeros = 18!) bytes of data every day, text analysis has become a key tool for structuring the data and getting the key insights. The organized and insightful data is worth millions of dollars in the present day scenario and it is no secret that Uber and Airbnb are so successful because of their massive data advantage. Harnessing data effectively enables companies not only to control costs and risks but also to compete more effectively and drive profitability by serving their end customers efficiently.
However, it’s easier said than done. Most of the organizations struggle to categorize the unorganized data and generate insights based on it. Not only the textual data but also the images, audios, and videos have become an integral part of information sharing in this digitally-driven world. Cleaning, tagging and converting this data into meaningful insights has added a level of sophistication in the way text analysis is being handled these days.
Earlier, it used to be nearly impossible for small companies to get hands on this kind of text analysis as either the tools available in the market were too overpriced or had to resort to low-end text mining giving them just a slice of the big pie. But the emerging technologies and the constant effort of the people to beat all odds has produced surprising results. The advent of NLP (Natural Language Processing) has armed each and every company with the means to analyze a plethora of data they have It empowered them to automate most of the processes involved in it thereby enabling them to directly fetch actionable information and thus saving both time and human cost.
Natural Language Processing (NLP) is the machine handling of written and spoken human communication. It consists of methods drawn on linguistics and statistics, coupled with machine learning, to model language in the service of automation. NLP employs a variety of methodologies to construe the ambiguities in human language, including the following: automatic summarization, part-of-speech tagging, sentiment analysis, feature extraction, relations extraction, as well as emotion detection. It takes into account all types of data gathered and fed, be it as simple as text or as abstruse as video files.
There are myriad applications of NLP when clubbed with text mining for businesses (or personal needs). Be it speech or text — with volume, velocity, or complexity sufficient to push you to seek an automated assistance — both can benefit from Natural Language Processing (NLP). Just imagine how dedicated algorithms can change the face of 80% unstructured business-relevant information around us. Moving forward I will try to illustrate the basic implementation and use case of various facets of NLP and how it can help us in text analysis.
Topics, Grammar, and Similarities
With the use of various statistical algorithms, various categories are determined which in more technical terms can be termed as “Classes of similarity”. Classification can be explained as the process by which various instances are clustered together into various classes (or groups) on the basis of various attributes. Generally, grouping can be of two types – one is conceptual classes, for instance, “smartphone companies” from Samsung, Nokia, Apple, Xiaomi, etc. Another class involves co-referencing – grouping similar instance in different categories of different classes. For example, “Lionel Messi is the captain of Barcelona FC. He was born in Argentina.” can refer to a different subset. It can be classified under Gender, Role, Nationality and millions of different classes. Even “He” word being used to refer Lionel Messi is also a information. One of the prominent methods of discerning relationships among entities is Syntactic Parsing.
Another lucky to have the feature of NLP in the world of text analysis is its spell and grammar check capabilities. Unlike Microsoft Word(or Google Docs) inbuilt spell checker, NLP based grammar and spell checkers are not limited to single error detection. For instance, normal spell check won’t identify two errors in “I went there at three o’clock.” Try using Stylus, on the other hand, one of the prominent interactive proofreading interfaces. A linguistic approach to grammar checking might involve resolving parts of speech. The process involves steps like sentence diagramming, part-of-speech tagging and study of syntactic relations.
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