Few things are potentially more powerful than sentiment analysis. Consider a few of the recent technological trends unfolding before our very eyes: open APIs, a precipitous drop in start-up costs, fascinating new data sources, and incredibly powerful new tools to visualize data. Against that backdrop, sentiment analysis promises to provide profound insights into many relatively uncharted areas.
There are more ways than ever to turn unstructured or semi-structured data into potentially useful knowledge. One such area may be food. Yes, food.
Better Nutrition through Data
According to its website, FoodMood is “an interactive information visualization project about food and emotion (two basic yet complex components of everyday life). The project aims to gain a better understanding of global food consumption patterns and its impact on the daily emotional well-being of people against the backdrop of countries’ GDPs and obesity levels.”
Here’s a screen shot of FoodMood’s interactive tool. Warning: it’s a total time suck.
Tool around the FoodMood site for a bit and you’ll quickly discover that, by itself, it isn’t going to solve the world’s hunger problems, at least as it is currently constituted. After all, the data for the site emanates from Twitter. Now, there’s certainly nothing wrong with Twitter. Still, with “only” about 230 million users, it hardly reflects a representative example of humanity–at least not yet.
We are living in an era of unprecedented possibilities.
I have little doubt that FoodMood will evolve–and spawn imitators in a wide variety of industries and areas. And this is a very good thing. Maybe FoodMood can be linked to price and caloric information with open data? Maybe it can integrate food-related stories like the Heart Attack Burger served in New York City? Tie in crop prices and/or futures? The possibilities are just about limitless. (For more on this, see my post on similarities between Big Data and dieting.)
FoodMood may ultimately prove worthless or even counterproductive but that’s hardly the point. New ways of visualizing and analyzing data continue to emerge. In the words of Heisenberg, “Nothing stops this train.”
What, if anything, is your organization doing about this now? And what are its plans for the future? If the answer is “nothing”, ask yourself what that means.
What say you?
A modified version of this post originally ran on the DataRoundtable.
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