How NLP Is Automating the Complete Text Analysis Process for Enterprises?
Let's explore how Natural Language Processing is automating the complete text analysis process for enterprises.
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in a world where we generate 2.5 quintillion (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.
what if there is a way to get the first-hand opinion on your style of writing? guess what, there are several proofreading tools available in present-day scenarios which help you in analyzing your writing skills. two more categories of stylistic analysis are lymbix which analyses email sentiment and another is automated social-comment moderation which is another less-explored application of nlp. text mining and nlp are widely used hand-in-hand for social media monitoring. the analysis is performed on a pool of user-generated content to understand mood, emotions, and awareness related to any topic.
summarization and translation of nlp
a summarizer is not just the identification of key lines or keywords. a summarizer needs to be able to generate a shortened version which conveys the entire meaning of the text. this is one of the features of nlp which is widely used by market researchers. the logic behind it is not incomprehensible totally. machine computes the relative measure of the significance of words and sentences by taking into account the statistical data like word frequency, distribution and many other attributes. sentences with the best significance scores are extracted and summed up to be the "abstract."
translation is another wonderful nlp application. each language has its own set of grammar, idioms, and syntax. a translator’s main motive is not just to convert the language but also to change it in a fashion that the resulting text makes sense. like summarisation, machine translation involves natural-language generation. google translator is one of the best examples of nlp based translation till date.
sentiment and speech recognition
another trending use of nlp is in sentiment analysis (also known as opinion mining). sentiment analysis is the automated process of understanding an opinion about a given subject from written or spoken language. it is one of the most promising fields within natural language processing(nlp) that builds a system to identify and extract opinions within a text. the main motive of this system involves:
- polarity: whether a positive or negative opinion is being passed
- subject: the topic of the conversation or context.
- opinion holder:person or source from which the opinion is being generated or expressed.
- and of course the opinion itself.
with the use of sentimental analysis systems, all available information over the internet (social media, review sites, forums, blogs, and many other opinion-generating platforms) is transformed into structured data of opinions and sentiments towards products, people, services, brands and even politics these days. (trump too says so :p). not only that, the business model of many big companies rely on this technology for market analysis, customer service, and even product build. there are innumerable real-time applications of sentimental analysis in the arena of the brand and social media marketing, market research, product analysis as well as customer service.
now that you know
now that we know a bit about the wonders that nlp can do and has been doing in the field of text mining and analysis, let’s sum-up with the business contexts. nlp is being used in conjunction with the collection, integration, and analysis of contrasting forms of online, social and enterprise data. in today’s world of heterogeneous big data, all the text and speech-extractable capabilities don’t work on their own. natural language processing (nlp) needs to be used for business analytics and also for activities like web-search which don’t involve non-textual or non-speech sources.
Published at DZone with permission of Shashank Gupta, DZone MVB. See the original article here.
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