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What is self-documenting analysis?

A classical annoyance of data analysis is the extra work required to properly record what you have done. Despite benefits of enabling others to pick up where you left off, or to help you revisit your work and still understand it later, the tedium of proper documentation tends to result in little to no documentation whatsoever. Subsequently, failure to document forms technical debt. The value of your acquired insight is…
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Challenges in Analyzing Online Communication

Online communication, also known as Computer-mediated Communication (CMC), is any communication among individuals via networked devices, such as computers and mobile phones1. Online communication is not only textual, but may include other modes of communication: images, audio, video, or a combination of those.  For example, we may participate in a Facebook conversation using a GIF animation along with text or we may use video, audio, and text at the same…
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Entity resolution in text analysis

In our analysis of textual data there are many different types of entities we need to identify and capture such as companies, organizations, places, currencies, stocks, and people. The difficulties in accurately capturing entities in text This task alone can be challenging, but in many scenarios it is further complicated by the fact that entities can be highly contextual and can be referenced in a myriad of forms. Referencing people…
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Normalization in preparation for fuzzy matching

When we need to compare entities stored as string data we often need to consider many different pieces of the string. For example, suppose we examine the following names in our data: Steven M Johnson Steve Michael Johnson When we, as humans, compare these entities, we don’t directly compare each string as a whole. Instead we compare substrings. “Steven” is compared to “Steve,” “M” to “Michael,” and “Johnson” to “Johnson.”…
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