Mining the Tweets

October 12, 2009
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I received through my Google Alerts a very interesting article: Twitter is in talks with Microsoft and Google regarding the use of data mining technology on user Tweets.

Despite the fact that Twitter execs do not appear so eager in making the deal as soon as possible, this news clearly show where things are going. If and when the deal is finalized it will be very interesting to see:

What kind of data and text mining techniques will be mostly used? Which of them will prove useful?

Many examples of what can be done in terms of data and text mining application on Twitter were given in this blog (starting from January 2009). In my opinion, types of analysis that will prove to be interesting – apart from sentiment mining for products and services which is already taking place – are cluster analysis (see post “Clustering the Thoughts of Twitter Users” here) and prediction of virality.

Although Twitter will be able to monetize through insights extracted from cluster analysis and opinion-sentiment mining, perhaps the most important analysis is finding patterns in user emotional states. Recall that everything needed for such an analysis exists in user tweets: life events, thoughts and their .



I received through my Google Alerts a very interesting article: Twitter is in talks with Microsoft and Google regarding the use of data mining technology on user Tweets.

Despite the fact that Twitter execs do not appear so eager in making the deal as soon as possible, this news clearly show where things are going. If and when the deal is finalized it will be very interesting to see:

What kind of data and text mining techniques will be mostly used? Which of them will prove useful?

Many examples of what can be done in terms of data and text mining application on Twitter were given in this blog (starting from January 2009). In my opinion, types of analysis that will prove to be interesting – apart from sentiment mining for products and services which is already taking place – are cluster analysis (see post “Clustering the Thoughts of Twitter Users” here) and prediction of virality.

Although Twitter will be able to monetize through insights extracted from cluster analysis and opinion-sentiment mining, perhaps the most important analysis is finding patterns in user emotional states. Recall that everything needed for such an analysis exists in user tweets: life events, thoughts and their associated emotional states. What emotions drive people in making several decisions such as which product to buy or which politician to support? What kind of feelings are generated during a bad economy? Perhaps by analyzing Tweets we could understand people (and thus consumers) in entirely new ways since this is the first time that this information is available to us.

How will Twitter users react when knowing their Tweets are being analyzed?

My first impression is that Twitter users do not care too much if companies extract the insights discussed above however this does not mean that people’s opinion will stay like this. Again, user reaction on this matter is something that could be changed anytime and should be looked at closely.

Which other technologies will be mostly sought?

Although no one can give a definitive answer, i would likely expect Natural Language Processing (NLP) and Ontologies to be also heavily used and sought as expertise.

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