Interpreting Alias table testing multicollinearity of model in R

Could someone help me interpret the alias function output for testing for multicollinearity in a multiple regression model. I know some predictor variables in my model are highly correlated, and I want to identify them using the alias table.

Model :
Score ~ Comments + Pros + Cons + Advice + Response + Value + Recommendation 
+ 6Months + 12Months + 2Years + 3Years + Daily + Weekly + Monthly

Complete :
            (Intercept) Comments Pros Cons Advice Response Value1
UseMonthly1      0           0    0    0    0      0          0                
             Recommendation1 6Months1 12Months1 2Years1
UseMonthly1   0               1        1       1             
             3Years1 Daily1 Weekly1
UseMonthly1  1         -1        -1    

Value, Recommendation, 6Months, 12Months, 2Years, 3Years, Daily, Weekly, and Monthly are binary categorical variables.
Score, Comments, Pros, Cons, Advice, and Response are numeric variables.

Can I assume UseMonthly is highly correlated with 6Months, 12Months, 2Years, 3Years, Daily, Weekly? What is the difference between the 1 and -1 values in the alias output? Is it positive and negative correlation?