measuring flip-flop behaviour across several topics 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: &lt;# of Pos words, # of Neg words>.

I think a good solution will consider how dense and ideologically clear the bad user behavior.

This image summarizes the previous description:
So I have a theory for this. We want to be able to distinguish users who are emotional and biased from those that are unemotional and unbiased. 
$$M = emotionality\\
B = Bias\\
t_i = tweet\ number\ i\\
b_i = bad\ words\ in\ t_i\\
p_i = good\ words\ in\ t_i\\
w_i = words\ in\ t_i\\
M = \frac{(b_i+p_i)}{w_i}\\
B = \frac{(b_i-p_i)^2}{w_i}\\
$$

M is the emotionality because it calculates the frequency that word is going to be an emotional one (instead of a descriptive one).

B is the bias because biased tweets will have more good (or bad) words than ones that hold both sides equally.

I don't know if this prediction will hold true, but i reckon if you plot each tweets x as bias and y as emotionality with a hue of good or bad, there will be two clusters (i am eager to be shown to be mistaken this certainly sounds like an interesting problem)!