Intent-action ontology and tone matching algorithm for organizing news articles

Many real life problems are about discovering and collating information. We have some scenarios and news artifacts and we need to derive the complete story. In case of assorted news articles related to same topic, we may need to derive the order of events with reference to their occurrence. Take example of daily newspaper, the news come as progress and we still could not build complete picture. To deduce conclusive evidence or to build holistic news one needs to organize different pieces of text. This research paper delivers a novel algorithm (Intent-Action Ontology based collation) for mapping multiple relevant news artifacts on time line by determining their interdependency along with personalized emotional traits. It takes in to account flow of events, continuity, negation, shift of emotions, association etc. along with individual's emotional tolerance to collate news and to produce desired impact. The proposed method helps to build news flow along with personalized emotional impact. This approach can be used for legal case flow detection and associating assorted news from the period under purview.