Information Technology Reference
In-Depth Information
Chapter 2
Twitter Sentiment Tracking for Predicting
Marketing Trends
Cagdas Esiyok and Sahin Albayrak
Abstract
We present a web-based Twitter sentiment tracking tool for brands. The
tweets about four companies, namely, Facebook, Twitter, Apple, and Microsoft are
collected by this system. The collection is implemented in an hourly basis in 17
Anglophone cities from which these tweets are sent. After collecting the tweets, the
systemclassifies themas positive or negative by using theNaïve Bayes andMaximum
Entropy classification methods. Later on, the system determines the winner brand
of each city according to the percentage of positive tweets sent by users located in
the aforementioned cities. Lastly, the winner brands of the day can be monitored
on a web page using Google Maps. To increase the performance of classification
methods, the tweet texts are preprocessed, such as through converting all the letters
to lower case, both for training hand-classified dataset and for the collected tweets.
Furthermore, statistical tracking charts can be viewed via web page of the system.
A dataset is produced by collecting 362,529 tweets in 9 days via Twitter API for
the research, which is automatically classified by the system. Performance of the
Naïve Bayes and Maximum Entropy classification methods is also evaluated with
the hand-classified dataset.
Carl Marks Is an Intern
Finally, holidays had started—no school for a couple of weeks. “Once we have
holidays, I will do nothing but chill in the sun,” he promised himself only weeks
before the last day of school. His parents weren't too happy about this attitude
though: “Carl, just one more year until you finish high school. I think you should
spend this summer holiday working as an intern somewhere”, his mom told him.
“Look at your sister. She didn't do anything last year and now, she has to do an
internship to find out what she is interested in”, his dad added. “She is losing a whole
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