# Inside a Consumer’s Mind with Text Analytics

> So far we have seen several examples on how Predictive Analytics applied in Social Media and Blog posts can help us suggest better strategies in Marketing, Branding, Sales and PR . This post is a walk-through example on how we…
> URL: https://www.smartdatacollective.com/inside-consumers-mind-text-analytics/
> Last modified: 2010-10-18T15:00:54+00:00

So far we have seen [several examples](http://lifeanalytics.blogspot.com/search/label/twitter) on how Predictive Analytics applied in Social Media and Blog posts  can  help us suggest better strategies in Marketing, Branding, Sales and PR .  This post is a walk-through example on how we can choose a concept,  extract what users write about this concept on Twitter, get insights on  how consumers think / behave about it and finally group similar consumer  thoughts and experiences using Cluster Analysis.  A “concept” could be:
– Any activity
– A Brand (e.g Apple Inc.)
– A Product / Service
– A Politician

and -almost- anything discussed in user Tweets.
What  we will look at is work that was made specifically for understanding  what consumers think, liked or disliked while visiting a shopping Mall.  What do people feel when visiting a Mall?  Which words are associated  with a positive experience or when a smiley is present in Tweets about  Malls? Using the Twitter API approximately 36000 distinct Tweets where  collected on consumer experiences from visiting a shopping Mall (sample  below shows an example of a consumer’s negative sentiment):

So how can an analyst get into a  consumer’s mind by analyzing Tweets and how would this information be  useful? To find some answers I teamed up with Marketing Strategist Dr Nikos Dimitriadis to assist me in the actionability and interestingness of each extracted  insight. Note that we capture thoughts from a biased sample  which  means that we cannot make inferences about the general population.  However this work can be a great additional tool for finding new ideas  and insights for Marketing initiatives -on top of more traditional  methods such as focus groups-  and also enables us to form several  hypotheses as to what could likely work.
After  a number of pre-processing steps to clean captured Tweets from  irrelevant information (such as links), replace words with their  synonyms and remove frequently occurring words such as ‘and’, ‘to’,  ‘at’, ‘in’ and ‘mall’ and also filter all Tweets having a small length i  started performing frequency counts of the words contained in Tweets  about Malls :

we immediately notice how often LOL  and 🙂 (smiley) appear in Tweets about being, going or returning from
the Mall which also gives us examples of consumers being in a specific  mood . Here is what happens when we look at the most frequently  occurring 2-word phrases:

and 3-word phrases (Note : ive = i’ve):

Looking at the two charts we also notice that we frequently find the phrases:
–   My best friend : since consumers Tweet the fact that are visiting a Mall with their best friend.

– My nails done : appears to be one of  women’s frequently discussed activity.

We  then could look at Words and Phrases that seem interesting in  understanding consumer experiences and values when visiting a Mall, such  as :
– Shop

– Shoes
– Parking Lot
– Food Court
– Need / Want
– Walk around
– Made my Day
– Post Picture FaceBook
and  mine through all these words / phrases to understand what consumers  think : What exactly made the day of consumers who used the phrase “Made  my Day” in their tweets? How do consumers feel when they visit the Mall  with their best friend? when they are alone? Which activities trigger  positive feelings? But more importantly : How could one use this  information to better understand consumers and Market a concept?
More on  the next post.