Text Analytics in Telecommunications – Part 3 by Themos Kalafatis.
From the post:
It is well known that FaceBook contains a multitude of information that can be potentially analyzed. A FaceBook page contains several entries (Posts, Photos, Comments, etc) which in turn generate Likes. This data can be analyzed to better understand the behavior of consumers towards a Brand, Product or Service.
Let’s look at the analysis of the three FaceBook pages of MT:S, Telenor and VIP Mobile Telcos in Serbia as an example. The question that this analysis tries to answer is whether we can identify words and phrases that frequently appear in posts that generate any kind of reaction (a “Like”, or a Comment) vs words and topics that do not tend to generate reactions . If we are able to differentiate these words then we get an idea on what consumers tend to value more : If a post is of no value to us then we will not tend to Like it and/or comment it.
To perform this analysis we need a list of several thousands of posts (their text) and also the number of Likes and Comments that each post has received. If any post has generated a Like and/or a Comment then we flag that post as having generated a reaction. The next step is to feed that information to a machine learning algorithm to identify which words have discriminative power (=which words appear more frequently in posts that are liked and/or commented and also which words do not produce any reaction.)
It would be more helpful if the “machine learning algorithm” used in this case was identified, along with the data set in question.
I suppose we will learn more after the presentation at the European Text Analytics Summit, although we would like to learn more sooner! 😉