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.)
After performing this analysis we essentially come up with a list of words and a metric which tells us the discriminative power of each word. Here is an example of identifying these words :
(Note : Results based on a very limited Data Sample)
Keeping in mind that results shown are extracted from a very limited amount of data, the decision tree depicted above shows us that :
The presence of word Dragi (which means “Dear” in Serbian) means that a post usually does not receive reactions. This makes sense as many posts that reply to subscriber questions start with the word “Dear” and then the first name of the subscriber is added.
novo (= “new”) is a word that receives a lot of reactions along with hocu (=”i want”) and dopuna (= recharging credit for prepaid subscriptions). In the same manner we identify more words that are selected to be important in discriminating interesting vs non-interesting posts. Note that we have to identify the correct context. For example we have to identify what the word novo refers to most of the time : A new cell phone or a new promotion? From the sample analyzed It appears that :
1) Subscribers “like” posts that discuss New devices such as Cell phones and tablets (The Next Step could be the identification of these devices)
2) Subscribers want new promotions (but we then need to find which types of promotions exactly)
3) Issues with incorrect re-charging are creating a very negative sentiment (but then we need to find which operator co-occurs with this sentiment and for which cases)
In this way we are able to better understand subscribers, extract the Topics that they are interested in and take all this information into account when creating future initiatives. Note that with this way we can have hints on several potential “hot” topics such as Cell Phone and Tablet Brands, Tariffs, Services, Marketing campaigns, and that this can be performed for each Telco Provider page which means that we can analyze and identify the “hot topics” applicable for each Telco provider.
All the above along with several other uses of Predictive and Text Analytics for Telecommunications i will present in the upcoming European Text Analytics Summit in London, UK.
In the event that a Marketing or PR Agency uses -as in the ways shown above- Social Media Analytics to identify hot topics in News, Sports, TV, Banking and Consumer Goods a Knowledge Base is created which has many uses : Imagine a scenario where a Telecommunications provider wishes to use a Sport event for a Marketing campaign. We could take into account the hot topics found from a “Sports” analysis and suggest ideas in a much more informed way. More for this on the next post.
