The powerful emergence of religious faith and beliefs within political and social groups, now leading to discrimination and violence against other communities has become an important problem for the government and law enforcement agencies. In this paper, we address the challenges and gaps of offline surveys by mining the public opinions, sentiments and beliefs shared about various religions and communities. Due to the presence of descriptive posts, we conduct our experiments on Tumblr website- the second most popular microblogging service. Based on our survey among 3 different groups of 60 people, we define 11 dimensions of public opinion and beliefs that can identify the contrast of conflict in religious posts. We identify various linguistic features of Tumblr posts using topic modeling and linguistic inquiry and word count. We investigate the efficiency of dimensionality reduction techniques and semi-supervised classification methods for classifying the posts into various dimensions of conflicts. Our results reveal that linguistic features such as emotions, language variables, personality traits, social process, and informal language are the discriminatory features for identifying the dynamics of conflict in religious posts.