While there may exist several types of analytics which processes data differently, a particular type of data processing which released in the 2000s but hasn’t been explored much is the text analytics.
A Pre-Warming up
We live in a much smaller and more compact world than our forefathers did. There lies beyond the inessential world; the virtual world. We interact, deliberate and even breathe over the social forums, supposedly less than we do in real life.
Some anticipate a near meltdown of real emotions in this technological mad rush. We instead, affirm our belief in the newer tools that still escape the fancies of common people. Text Analytics still remains one of these little lesser known wonders. Text analysis is not exactly unexplored, but much needs to be said about its utility and scope.
Text Analysis Explained
For a fairly plain definition, text analysis is the process of derivation of high-end information through established patterns and trends in a piece of text. This is done by a structuring process within the relevant data, which is eventually interpreted to give the final result.
The internet is brimming with information and to process the same could be quite a task. This task can easily be executed using Text Analysis Tools. The data scoop on the web will only be of some optimal use if we know how to crack and classify it effectively.
The logical outflow of the said data is multi-faceted. It could be decoded linguistically, statistically, sentimentally, semantically or taxonomically. To be honest, any information is a lame duck if we can’t interpret it in a proper fashion.
Machine learning technologies are used to extract hidden/abstract information from a piece of text by a computer. The result is groundbreaking because we find out untapped trends and opinion which have unbelievable implications in Research and Development, Marketing and Advertising, Intellectual Property, Intelligence, Life Sciences and Social Media Monitoring, to name a few. Talk about killing many birds with one stone!
Let me illustrate how
We are witnessing a huge disruption in every industry due to the acceptance of Social media as a platform to share views and comments about anything including big brands, people, product etc. Users now take it to platforms like Facebook and Twitter to share their sentiments.
Social Media is now filled with millions of reviews about a single product, think about a brand now. Therefore, it becomes imperative for any brand to run a sentiment tool in the background to know the sentiments of all the people in order to serve them well.
A marketing professional can know about all trends and opinion pertaining to his potential competitors. What people feel and say, is strewn all over the internet. But who will take the pain to evaluate and study all that? Let’s face it- Text Analysis Tools in this context can be a potential cash cow for this marketing chap!
Text Analysis Tools
Guess what? The bourgeois examples which I illustrated are not the only plausible uses of Text Analysis. In fact, many varied tools which ease the process of Text Analysis have been anchored by young techies. Now that we have a fair idea of how Text Analytics work, we shall delve into their classification.
The tools established by us include Semantic Analysis, Entity Extraction, Sentiment analysis, Taxonomy Classification and Keyword Extraction.
Semantic Analysis aids in clustering similar pieces of text by understanding the meaning of different contents and streamlines research by eliminating redundant text contents. We can easily measure the level of similarity and relatedness of different pieces of texts. I don’t think its wide usage in research deems necessary an explanation.
Entity Extraction locates and classifies elements in text into definitive categories. For instance; the name of people, organizations, places, etc. There can be many typified entities in your content and that is where we need Entity Extraction. This practically banishes scope for ambiguity. Aloha! You will finally know that Mango cannot just be a delightful fruit but a high fashion clothing brand. The extracted entities can have significant importance in market research about any brand, person or company over the web.
Sentiment Analysis, as fancy as it sounds, is nothing but opinion mining of text content which recognizes and extracts subjective information in the relevant data. We provide a Sentiment Analysis Tool; which provides a highly apt and accurate analysis of the overall sentiment of the text content which can be widely applied to reviews and social media for a variety of uses, ranging from marketing to customer service.
The social media includes a zany network of Facebook statuses, twitter updates and varied reviews. These posts and updates bring with them deep-seated emotions, which users might associate with different brands and companies. This is explosive information for say, a PR consultant or a marketing specialist. The Sentiment Analysis tool is again highly useful in Social media monitoring, market research, tracking company/product reviews, studying public opinion. It also helps the financial traders and analysts in making better choices.
Taxonomy classification is a highly important tool for classifying data sets on a large scale. It clusters the concept of the definition of documents with a hierarchical approach to a self-organizing map. We utilize a comprehensive list of taxonomy to categorize the text content or webpage contents into definitive tags. Taxonomy classification could make way for achieving excellence in focussed research, recommending content, knowledge management, text mining, information retrieval, content summary and cognitive filtering.
Keyword Generator is a tool which can be employed for indexing data, generating tag clouds, accelerating the searching time. Our tool helps to find and suggesting keywords in a text and ranking them. It will generate an extensive and exhaustive list of most relevant keywords and keyword phrases. It is a potent hit for recommending content, focussing search, knowledge management, information retrieval, text mining, content summary and cognitive filtering.
I hope you found this article useful. This was originally published here.