The analysis of travelling trends, using tweets data mining.

   In our previous studies, we showed that it is possible to use the data mining of Twitter microblogs for events forecasting (see Eurovision 2013 forecasting), goods marketing, stock market analysis (see article at arxqv.org).

   In this research, I’m trying to show that tweets mining is possible to use in the sphere of services. As an example, we take the theme of travelling.

   So, we analyse the messages of Twitter microblogs concerning the “travelling” themes. In our analysis, we use the theory of frequent sets and association rules, the theory of semantic fields. We conducted the analysis using the R language and corresponding special-purpose packages.

   For the testing, we take such semantic frames as: time of a trip, city, country, and some associated key concepts.  For the analysis, we downloaded the tweets for the last week of May.

We have found the following countries for travelling, which were mentioned most often:

 

The similar calculations for cities:

             The data mining for months is described on the following diagram:

 

 Our further analysis can be made or each separate month:

 

 

The similar data maning was made for tweets posted by users,  located in London and New York:

 

Users from London:

 
Users from New York:

 

 

 

Using the theory of frequent sets, we can find the cities, which are often mentioned together. These frequent itemsets can be displayed by the following graph:

 

 

   In the loaded tweets, you can find the following association rules:

 

             For each of revealed trends or association rules, it is possible to find a list of users, whose messages create these trends and rules. Such list of users may be used for target marketing.

             The research conducted shows the availability of using the data mining of Twitter microblogs for the marketing research of services, in particular, travelling.