Sunday 4 p.m.–4:40 p.m.
Social Media Brand Positioning Workflow
David Gerson
- Audience level:
- Novice
Description
Social Media is becoming ever pervasive in modern culture. One of the simplest use cases of social data is as a "temperature check" for how a brand is performing. This talk offers a simple walk-through of how python can be used to take Social Data from its raw form and transform it into a usable visualization to help understand the market a company exists in.
Abstract
This project focus on how social media data extracted about the fast food industry can simply and repeatably be turned into a system for analyzing market position. This market position can by proxy be used to derive strategy.
- Data will be extracted from Twitter for a variety of brands that operate in the same market space.
- That data will be split into its individual words and using nltk it will be cast to its root word.
- A TFIDF (sci-kit or raw code) analysis will be used to identify which words are uniquely used to describe the industry.
- The data will then be topic modeled in gensim to further reduce the number of dimensions and to reduce the number of feature vectors that need to be tracked.
- Finally the data will be reduced into a 4-plot using Correspondence Analysis or MDS.
While these are all independently simple the analysis itself is extensible enough to solve this problem in a variety of circumstances.
