- 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.
One additional interesting insight is the knowledge of what makes a Twitter user having many followers. Consider the following questions :
- 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.
One additional interesting insight is the knowledge of what makes a Twitter user having many followers. Consider the following questions :
- 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.
