The Semantic Brand Score (SBS) is a novel metric designed to assess the importance of one or more brands, in different contexts and whenever it is possible to analyze textual data, even big data. This page presents a Python demo to calculate the Semantic Brand Score (its most basic version).
The notebook with the demo code is in this GitHub repository, click here to see it.
More detailed descriptions of the metric and of this code are available here and on the metric website semanticbrandscore.com.
At this link you can find an example of a more complete dashboard of results.
The following Python packages are required to run the SBS Demo script: networkx, pandas, csv, re, nltk, numpy, string, collections. Keeping all the process in memory is not recommended for big data. Please also consider using more efficient modules for network analysis, such as Graph Tool.
Please cite the following paper while using the Semantic Brand Score: Fronzetti Colladon, A. (2018). The Semantic Brand Score. Journal of Business Research, 88, 150–160. https://doi.org/10.1016/j.jbusres.2018.03.026
Feel free to contact me and suggest edits.