Social Media Analytics

Not all companies are at a stage where it makes sense to invest in infrastructure or personnel to help solve and integrate proper social media analytics. ZData provides hosted data analytics environments tailored to your company to get to insights quickly without the hassle of managing servers or DBA’s.  ZData Inc. will provide custom solutions that will go beyond accessing, processing and aggregating the social media data to provide a deeper analytical solution.  Our service considers a more robust analytical framework to extract more information out of the data and provide a reliable statistical analysis.  Below are two aspects that we consider to be valuable towards a meaningful analysis strategy:

1.  Characterization of the data sources:  Determine or inference who is generating the data, considering that not all data sources or data generators are necessarily equal in drawing conclusions.

2.  Characterize the flow of information through media networks:  Utilize the “social” aspect of social media data to determine how the information reaches various sources and people over time.

The ability to utilize the data and information from social media sources has become an increasingly valuable commodity for informing business, marketing, and other entrepreneurial decisions.  The tools and resources available to access and analyze such data sources have begun to grow at a rapid rate, but many of these solutions have either too broad or limited view in achieving specific goals.

A popular and general strategy simply aggregates, organizes, and counts the number of “mentions” of a specified topic or subject in the data.  The use of sentiment analysis has also become a standard approach, whereby the “feeling” about a particular topic is inferred from a corpus of social media text.  While these solutions have addressed some of the logistic issues in accessing, processing and aggregating the social media data, the analytical aspects are limited.

The intersection of the increasing data growth and economics has produced new ways to structure and store incredibly large volumes of data with Apache Hadoop. For most companies however, Hadoop is not a complete, single solution for analytics but part of a hybrid data pyramid with a tier of raw data stored inexpensively in Hadoop; a secondary tier of key data aggregated out of Hadoop and placed in traditional data marts, and a third tier of data required for speed-of-thought response times residing in memory. As part of this data pyramid, Hadoop, together with front and back end applications and tools like Alpine Data Miner that assist in data loading, transformations and analytics, can dramatically lower big data analytics costs without any compromise in business performance. ZData expertise leverages the latest technologies to provide a custom solution that can meet any enterprise analytic needs. Below is an example of a ZData hosted workflow.

The ability to utilize the data and information from social media sources has become an increasingly valuable commodity for informing business, marketing, and other entrepreneurial decisions.  The tools and resources available to access and analyze such data sources have begun to grow at a rapid rate, but many of these solutions have either too broad or limited view in achieving specific goals.

A popular and general strategy simply aggregates, organizes, and counts the number of “mentions” of a specified topic or subject in the data.  The use of sentiment analysis has also become a standard approach, whereby the “feeling” about a particular topic is inferred from a corpus of social media text.  While these solutions have addressed some of the logistic issues in accessing, processing and aggregating the social media data, the analytical aspects are limited.

What is this Sentiment they speak of?

  • Unstructured Text Data
  • Using computational linguistics to accurately determine the attitude of a writer and with respect to a topic


Why should we care?

  • Use “Opinion Mining” to predict political bias

Approach

ZData will provide custom solutions that will go beyond accessing, processing and aggregating the social media data to provide a deeper analytical solution.  Our service considers a more robust analytical framework to extract more information out of the data and provide a reliable statistical analysis.  Below are two aspects that we consider to be valuable towards a meaningful analysis strategy:

1.  Characterization of the data sources:  Determine or inference who is generating the data, considering that not all data sources or data generators are necessarily equal in drawing conclusions.

2.  Characterize the flow of information through media networks:  Utilize the “social” aspect of social media data to determine how the information reaches various sources and people over time.