Semantic oppositeness is the natural counterpart of the rather more popular natural language processing concept, semantic similarity. Much like how semantic similarity is a measure of the degree to which two concepts are similar, semantic oppositeness yields the degree to which two concepts would oppose each other. This complementary nature has resulted in most applications and studies incorrectly assuming semantic oppositeness to be the inverse of semantic similarity. In other trivializations, "semantic oppositeness" is used interchangeably with "antonymy", which is as inaccurate as replacing semantic similarity with simple synonymy. These erroneous assumptions and over-simplifications exist due, mainly, to either a lack of information, or the computational complexity of calculation of semantic oppositeness. This dissertation considers the following question: How can we convert the linguistic concept of semantic oppositeness to the computing domain? To answer this question, we follow the linguistic definition of oppositeness and develop a novel methodology based on antonymy as well as similarity. We also propose a novel method to embed the obtained semantic oppositeness in a vector space for increased generalization and efficiency. We then consider two realms of applications: inconsistency and disagreements. The inconsistency application helped us track changes in a medical research domain. The disagreement application accentuated the ability to detect rumours in the social media domain. Finally, we extract the commonalities and patterns in these methodologies to provide a comprehensive summary and a set of recommendations and future work. This dissertation is a culmination of previously published, co-authored material.