Saturday, May 7, 2011

Mining Tweets Related to Factory Farms

While social media has allowed farmers to organize and communicate about their industry, it also provides a rich data source for measuring sentiment or perceptions about their industry. Companies are finding that by mining text from web pages, comments, blogs, and social media, they can measure consumer perceptions almost as well or better than they can through explicit surveys. These powerful analytics could be very beneficial to those in the ag industry or advocacy groups.

I thought I’d take a stab at mining tweets related to ‘factory farms’ using the 'tm' package from R.

I extracted about 2000 tweets containing the term ‘factory farms’ and produced the following cluster analysis on the text:

This seems to give an idea about the content of conversations regarding ‘factory farms.’  Some of these appear to center around gmo foods and Monsanto. This already informs me of perceptions about ‘factory farms’ and biotechnology. It seems there are separate clusters of conversations, some related to Monsanto and gmo’s, others related to food and livestock production in general.

It also appears that the topic of ‘factory farms’ is often discussed by the #agchat group, and other food and animal related issues.

I also ran some correlations, or ‘word associations.’  Terms that tend to be used in association with ‘factory farms’ include hens, debeaked,suffering, cruelty,secretive, excess.  All of these terms tend to be related to livestock production, and seem to have negative sentment.  Words correlated with family farms are more neutral, hauled, Missouri, beans, peas, operated, battling.   Terms associated with ‘gmo’ include ban, irreversible, killing.  

Of course, this is just a first jab at this, I’m no expert in text analytics, and I had to rely on my subjective interpretation to some extent.  However, more sophisticated analysis is possible and could be more revealing than the example I just gave.

For another example with R code see Mining Tweets About the Budget Compromise.

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