Sentimental Analysis: What is it?
Think over this… A scene from our normal routine life..
Feeling happy! Check-in: Goa, India.
This Facebook post is a sufficient raw data for an elaborate sentiment analysis.
For a layman, the “feeling list” of Facebook is just a stack of emotions piled up from which he can choose his current state of feeling or to be sarcastic enough, can tease his not-so-good “friends” on Facebook. One step forward, this data acts as a foundational power-source for many industries such as travel and tourism which conduct extensive data mining on such posts on Facebook, which in their terminology is called as Sentiment Analysis.
Sentiment Analysis and BigData..
Also known as Opinion Mining, sentiment analysis and bigdata solutions work hand-in-hand to ensure effective Customer Relationship Management (CRM) and commercial success. Wikipedia defines Sentiment Analysis as “the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. Sentiment analysis is widely applied to reviews and social media for a variety of applications, ranging from marketing to customer service.”
Posts, Tweets, Pins – BIGDATA’s Sentiment Analysis
The current need of extracting emotional information from the social arena, though largely virtual, was never a way with this world, before bigdata solutions and hadoop revolutionized the entire setup. Bigdata hadoop has been chiefly instrumental in adding an additional valency to the social data gathered from social sites such as Facebook, Twitter, Pinterest, Instagram, while simultaneously amalgamating technology with business pursuits for mutual benefit. With machine learning and other tools such as listening tools and sentiment analysis, bigdata hadoop has been catering successfully to the business world, whereby it digs out meaningful information from millions of Facebook Posts, Twitter Tweets, and Pinterest Pins.
Big business houses and social sites use bigdata hadoop for storing, reporting, and processing information such as “how many people checked-in Goa during new year celebrations?” Not only are the business houses, hotels, and the aviation industry making best use of this mined data, but also the social sites such as Facebook. One can gauge the value of bigdata in social media analytics if one goes through the “feeling list” of Facebook – the list with a number of emotions: positive or negative, made calculatingly mathematical for sentimental analysis, since the computing language fails to take into consideration jumbled-up human emotions. Though the computational linguistics widely accepts binary statements such as “the flight was comfortable but did not like the food served on-board,” Facebook has crisped its list of emotions felt in order to reduce inaccuracy to minimum, thereby increasing the efficacy of data collected, which has been reported as 80% authentic and meaningful.
Facebook-Walmart – BigData’s Social Genome
The coalition between social media – a storehouse of unstructured data, and bigdata is undeniable. A quotable instance comes from a “deeper” interwoven relationship between the retail giant Walmart and the unbeaten social site Facebook. Walmart-Facebook collaboration has Social Genome, a Bigdata analytics solution, at its disposal. The social genome project processes millions of posts and likes on Facebook, in order to provide analytics solutions to Walmart, whereby Walmart is able to reach and communicate to its millions of customers.
In its own way, social genome project is not limited to social media only, since it encapsulates other fields concerning human behaviour and anthropology. It therefore, relies upon conventional ways of data collection such as surveys, and makes bigdata work on such a huge data for meaningful information. It is equally true that Bigdata solutions have been widely employed by digital marketing firms and by other commercial houses for effective marketing campaigns. Bigdata solutions work like magic wands on unexceptionally huge, unstructured, and raw data gathered from various sources such as Web Mining, Search Information, Social Media, Crowd Sourcing, Mobile Apps, Transactional Information, and in its turn crystallizing such information into meaningful data that can be further processed for commercial success.
That’s why BigData Professionals are in Huge Demand!
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