In cooperative or hostile environments, agents communicate their subjective opinions about various phenomenon. However, sources of these opinions may not always be competent and honest but more likely erroneous or even malicious. Furthermore, malicious sources may adopt certain behaviors to mislead the decision maker in a specific way. Fortunately, the reports of such misleading sources are correlated to ground truth. In this work, we propose to learn statistically meaningful opinion transformations that represent various behaviors of information sources. Then, we exploit these transformations while fusing opinions from unreliable sources. We show that our approach can be used to determine set of transformations that may lead to more accurate estimation of the truth.