Twitter Analytics: Words that make a difference

• Sentiment Analysis and Ontologies

• Analyzing the biographies of Twitter users and identifying clusters of similar users.

• Cluster Analysis on the thoughts of Twitter users

• Identifying the values and beliefs of Twitter users.

• Are there words that could potentially decrease the popularity of a Twitter account?

• How important is to have an actual photo (and not the default o_O photo)?

• Which interests or professions tend to be associated with many followers?

• How important is to have at least a small text of biography information?

• Sentiment Analysis and Ontologies

• Analyzing the biographies of Twitter users and identifying clusters of similar users.

• Cluster Analysis on the thoughts of Twitter users

• Identifying the values and beliefs of Twitter users.

• Are there words that could potentially decrease the popularity of a Twitter account?

• How important is to have an actual photo (and not the default o_O photo)?

• Which interests or professions tend to be associated with many followers?

• How important is to have at least a small text of biography information?

The first analysis that was performed was to identify whether specific keywords that exist on user biographies seem to be associated with a large number of followers. A second type of analysis was performed only with numeric data (such as number of re-tweets, number of user replies, number of updates,etc). Then a third type of analysis uses both a vector of keywords plus numerical data. Since a lot of work is needed, the process (but not all results) will be presented during the next posts.

FYI : Users that tend to use a lot the words “boredom”, “boring” or “bored” tend to minimize their chances of being popular.