Tweets mining using NLP can help in goods marketing.

   Recently the topic of data mining of messages in social networks, particularly in Twitter, has been widely discussed, from the point of view of sociology, politics, economics, marketing, etc.

In my previous studies, I have analysed Eurovision 2013 forecasting, tweets miner for stock markets, Granger test for financial tweets, the "End of the world" concept in twitter microblogs, the use of data mining for sports events forecasting.

  In this study, I want to show that tweets mining  can be valuable for the marketing research of goods. I took smartphones as an example. I have downloaded several thousands of tweets that refer to smartphones and contain the keywords like: iPhone, Apple, Galaxy, Blackberry, etc.  To study the iPhone concept, we used the theory of frequent sets and association rules. The research was conducted using the language of statistical calculations R.

Here are the results obtained:

 We received a matrix for aggregated association rules for concept 'iphone':

The association rules for concept 'iphone' with high support can be represented with the help of such graphs:

The frequent sets for concept 'iphone' with the high value of support can be represented by the following graphs:

Here is an example of associations for different smartphones :

The popularity of different models:

The comparison of two models based on tweets mining, using syntactic parsing: