I'm trying to analyze a behavior called "sentiment flipping" of users in a dataset, but I'm not able to step on.
Let's suppose that I have two groups of users, say them good and bad users.
My dataset contains N tweets that classified into 6 topics. The tweets were created by the bad and good users.
The 6 topics are about general issues, but 3 of these topics are about organization/individuals supported (A) by the "bad" users and the other 3 are against (B) their ideologies.
The difference between the bad and good users in their tweeting behavior is:
- The good user posted tweets in some of the topics (and maybe all of them) without forcing "positive" or "negative" sentiment in the topics.
- The bad user posted tweets contain negative sentiment on the topics against her/his ideologies and positive sentiment on the topics she/he supports. The clear difference between both users also is that the bad user posts negative sentiment profusely on B topics and positive sentiment on A topics.
How can I measure/show this flipping behavior in a score/value; given that each tweet is represented by a vector like: <# of Pos words, # of Neg words>.
I think a good solution will consider how dense and ideologically clear the bad user behavior.
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