How much time do you think it would take to analyze Twitter sentiment for a brand? A lot. After you’ve imported all of the data via Datameer’s Twitter Brand Sentiment application (found in our Datameer App Market), you would then have to tokenize the language of those tweets to create single words, attach a sentiment dictionary to determine the tone of those tokens, join those tokens to the sentiment dictionary, and build filters to find language trends by location or otherwise.
I’m exhausted. You now have a large number of worksheets and a lot of manual work ahead.
Welcome to the new text mining functions in Datameer 4.1! The Analyze Polarity function is just one of many that makes text analysis significantly faster and a lot more simple. In this case, just add in the function, point it to the text of the tweet and ta-da! All of that work you saw in the original workbook has now been condensed to a single column AND it can look at all of text and common sentiment symbols to determine if it’s positive, negative or undecided.
So, if I’m a large brand and I want to see how people in Boise, ID are tweeting about my product compared to people in San Diego, CA, I’ve found a very simple way to analyze the data.
Enjoy! And of course, don’t forget to download your free Datameer trial to try it for yourself.