Correlation and causation can be hard to determine, but the ability to visualize data extracted from text can drastically improve the likelihood that the insights you draw from data are actual trends, not just the result of coincidence.
Knowing where your users are is far less helpful than knowing which ones like your product instead just happened to have bought your product based on the marketing.
The above image is a map of the sentiment of comments about a large dating app. Only comments where the person mentioned a city, county, or other location were included which left 4763 data points.
Sentiment analysis is mapped with red being negative, green neutral, and blue positive.
There is a lot more blue on the east coast, and while the west coast is neutral there are some positive and some negative.
This chart shows a lot of information about adoption, market fit, and user experience. No machine learning required.
Where ML shines is that if this map is combined with the number of single men vs single women in a given area, an ML system can see that the highest satisfaction comes from places with the most single women.
This map was generated using data output of Recognant from processing 35,000 web pages, and then visualized with OpenHeat Map.
We are under NDA with client, but they agreed to allow us to share the visualization.