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Big Data and Social Listening
December 11, 2015 Blog big data data science

This article was originally published by datamation.com and can be viewed in full here

Through the power of the Internet, we now have approximately three billion mouths speaking and six billion ears listening. But even with twice as many ears as mouths, filtered listening is a science. Data scientists combine filters, artificial intelligence, natural language processing, machine-based learning, and text analysis to make sense of the passel of data facing them.

Social media data scientists use those same tools to process the data extracted from the Internet’s various social media platforms. Monitoring those platforms in real time for specific data and trends is known as social listening.

Social listening is a relatively new science that attempts to filter through the noise in order to extract meaningful, actionable data from blogs, reviews, online newspapers, articles, tweets, and posts. Anything posted as text can be scraped and analyzed against a set of data sieves to bring order out of chaos.

The purposes behind social listening are simple: To extract unsolicited opinion, to gather real world case studies, and to examine sentiment about products and services.

Using big data analytics, social media scientists can gather and extract pertinent data from many sources simultaneously and produce the initial results within a few days and keep capturing data in real time to keep the reporting fresh and accurate.

With social listening and analytics you can uncover your customer’s or audience’s pulse. Topics, communication channels, entities, trends, author information, keywords, concepts, and store everything in a data warehouse that be analyzed with trends over a period of time can be identified. Feedback on products, services, competitors, industry trends, and other parameters can be looked at.

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