فروشگاه و مرجع فایل و پروژه های دانشگاهی

Title: Creating Usable Customer Intelligence from Social Media Data: Network Analytics meets Text Mining

File type: PDF

Number of pages: 18

File size: 2.20 MB

Summary:

“Water water everywhere and not a drop to drink”
The Rime of the Ancient Mariner, Samuel Taylor Coolidge
In today’s world of social media and the wide variety of social media channels available, there is a huge amount of data available. The challenge comes in accessing that data and transforming it into something that is usable and actionable. Generally, organizations want to use the social media data to understand the needs and behavior of their customers or specific targeted groups of individuals with respect to the organizations’ current or future products or services. There are three major approaches to looking at social media – channel reporting tools, overview score-carding systems and predictive analytic techniques (primarily text mining). Each has its useful aspects, but each also has limitations. In this paper we will discuss a fourth approach – using a predictive analytic platform that includes not only text mining, but network analysis as well as other predictive techniques such as clustering to overcome not only the limitations of the previous techniques, but generate new fact based insight as well. This approach was first used at a major European Telco.
To explain the detailed approach, this white paper will work on publicly available data. We show not only sentiment analysis and influencers, but we are able to combine these techniques and – in our example – prove that participants who are very negative in their sentiment are actually not highly regarded as thought leaders by the rest of the community. This is an amazing result which goes against the popular marketing adage that negative users have a very high effect on the community at large.
Note: To enable our approach to be repeated by the reader, we used the KNIME open source platform throughout this white paper. Sample data and workflows showing these techniques are available on the KNIME site at WWW.KNIME.COM .