Mining for Gold: Social Media Text Analytics
Social media text analytics is a powerful tool that can provide businesses with insight revealing the sentiment of their brand, competitors and overall industries. Also known as text mining, text analytics involves extracting, analyzing and interpreting static and dynamic text across large platforms, and tells us hidden information we wouldn’t be able to gauge by manually scanning the countless text posts, reviews, and comments across the internet.
I currently work at a small contemporary art museum outside of New York and while exploring social media text analytics on Sprout Social, I decided to target my focus to the art and museum industries to explore what keywords would help me understand what people are talking about in these online spaces and communities, specifically on Twitter using Sprout’s Twitter Keyword Analysis function.
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Taking a look at a time range from the beginning of January to the end of September of 2022, my first choices while looking for words were “art,” “artists,” “contemporary art,” and “museum.”
I then researched to find insight into popular art-related keywords, since I wanted my results to be broad to cast a wide net, but still specific enough to the niche I was looking at. I found that “gallery” and “painting” were both high performers. I also discovered that “contemporary art” and “museum” were not as popular, so I decided to remove “contemporary art” and replace it with “gallery.” I did keep “museum” because it still felt relevant to track and added “painting” to my list.
The first data set shown is Keyword Volume. This gives us a look at the amount of times each word was used in the 9-month period of time in a line graph. The highest was the word “art” and then “artist.” Followed by “painting,” “gallery” and “museum.” The difference between the first two is that art and artist apply to more than just visual artists, so their volume was higher, ranging approximately 10 to 25 million. However I would say that “painting,” “gallery,” and “museum” are a bit more specific, so they had a similar volume under 5 million.
The next set of data was Share of Volume. The data is depicted in a pie chart and provides percentages for each volume and how much each keyword trended throughout the year. For example, we can see that the word “art” occupies 60% of the chart, but we also see that the trend percentage depreciates by -4%. Meanwhile a word like “museum” took up a mere 4% of the chart but its trend was up by 8%.
The last set of data Sprout provides is Stats by Keyword. This section has more statistics for each keyword, with a dropdown button that shows a data visualizer in the form of a line graph. For each word, we’re able to see the average use per day, total volume, and growth trend. The line graph provides the same Keyword Volume data shown in the top section, but this time for each keyword.
This was one of the most useful and interesting aspects of Sprout Social’s Twitter Keyword Analysis because of another feature it provides– top tweets from the keywords’ peak day.
I found that the more broad words I used were more difficult to connect with visual art. For example, the word “artist” peaked on August 28, 2022 and while observing tweets from that day, it was the day of the MTV Video Music Awards. So, while broad terms are helpful in many ways, they can also be a little too vague for the specific topics you may want to see.