How does correlation work in text mining? When we are done in pre-processing the text (removing stop words, stemming, lower case) then creating a term document that has TF-IDF as weight.
How can I decide what correlation test to do between my terms and the target variable?? I want to know what terms are affecting my target. My target variable is continuous from 1 to 100.
For example, when I used Pearson correlation it did not making sense to me as I  am not sure what is an increase or a decrease for a word used.
Edit:
I did a scatter plot for the function score and TF-IDF for the term "word". Since the TF-IDF is sparse and has alot of zeros , the plot didnt seem to be linear or make sense... how can I proceed if I want to find the correlation ?

 A: Pearson correlation is normalized covariance. Square the Pearson correlation to get the coefficient of determination, denoted $R^2$ or $r^2$ and pronounced "R squared". $R^2$ is the proportion of the variance in a dependent variable that is predictable from the independent variable(s). In other words, $R^2$ relates an explained fraction. For example, if $R^2=0.8$, the word used would explain 80% of the variance of the thing you are correlating to.
The sign of the correlation coefficient indicates how the dependent and independent variables relate. For example, a negative correlation means that decreased word usage suggests an increase in what you are measuring.
There are likely better things to look at to do what you want to do, but this answers your question and is a good start.
Edit After seeing the plot, it may be that the correlation is not significant. It may also be that Spearman's rank correlation would not be significant. At a minimum, testing for significance of correlation should be performed.
