Unbound

Inside a consumer mind with Text Analytics

... Themos Kalafatis/bigdata-madesimple:

In this post, I will walk you through an 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.

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 with small length.

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 

Screen+shot+2010-10-06+at+12.32.47+PM

And 3 word phases

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

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Looking at the two charts we also notice that we frequently find the phrases :

- See more at: http://bigdata-madesimple.com/inside-consumers-mind-text-analytics/?utm_source=email&utm_medium=email&utm_campaign=BDMS-Weekly-Newsletter-July22&utm_content=BDMS-Weekly-Newsletter-July22+CID_68d87e83309f5a3b21919e8cbad8383b&utm_source=Email&utm_term=How%20to%20get%20inside%20a%20consumers%20mind%20with%20Text%20Analytics#sthash.aMvCdYAc.dpuf

phrases :

Screen+shot+2010-10-06+at+12.32.47+PM

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

r

Looking at the two charts we also notice that we frequently find the phrases :

- See more at: http://bigdata-madesimple.com/inside-consumers-mind-text-analytics/?utm_source=email&utm_medium=email&utm_campaign=BDMS-Weekly-Newsletter-July22&utm_content=BDMS-Weekly-Newsletter-July22+CID_68d87e83309f5a3b21919e8cbad8383b&utm_source=Email&utm_term=How%20to%20get%20inside%20a%20consumers%20mind%20with%20Text%20Analytics#sthash.aMvCdYAc.dpuf

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 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

 

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