In 2001 I visited Trillium where I had a very useful seminar on Data Cleaning, Data Quality and Standardization during which the pareto principle became -once again- evident. When someone wishes to standardize entries in a Database so that the word “Parkway” is written in the same way across all records, he might find the following distribution of “parkway” entries :
15% of records contain the word “Parkway”
3% of records contain the word “Pkwy”
0.2% of records contain the word “Prkwy”
0.01% of records contain the word “Parkwy”
What that essentially means is that with a single SQL query one can find and correct 15% of “parkway” word synonyms to whatever standardized form is needed. But for the remaining variations one query solves only a very small fraction of the problem and this in turn increases the amount of work required, sometimes overwhelmingly.
In capturing and analyzing natural language we are confronted with the same problem : 60% of people might be using the same phrase for describing the fact that they don’t want to go to sleep with a simple “I don’t want to go to sleep”. But another 20% might be using something like : “i don’t feel like sleeping” and another 10% something like “i don’t want to go to bed right now”.
So we immediately see one of the issues that Text Miners face : The fact that we can use different phrases to communicate the same meaning. If we wish to analyze text information for classification purposes -say the sentiment of customers- we could achieve a 60-65% accuracy in our results with some effort. For a mere 4% increase in accuracy -from 65% to 69%- the amount of extra effort required could prove prohibitive.
Consider the following chart :
These are all examples of phrases people use in their everyday talk. We can visualize such phrases starting with” I don’t want to” and then each branch adds a new meaning to the phrase. So branches marked with numbers are the parts of speech that give us an idea of what a person doesn’t want to do : To go, to feel, to visit,to know. Things are getting much more difficult in terms of the effort required if we wish to add more detail -and probably insight- to our analysis by moving further down the branches in our sentence tree.
Perhaps for marketeers, the ability to quantify the distribution of words on the 1st level of the tree depicted above could be enough : If we end up with the following words distribution :
To feel : 15%
To know : 7%
To go : 1%
To visit : 1%
Then, we get an insight on which words to use to market products more efficiently.
On the next post we will go through a hands-on example of analyzing the thoughts of Twitter users and specifically what people seem to “don’t want”.
