  Linguistic relations in oral conversations present how opinions are
constructed and developed in a restricted time. The relations bond ideas,
arguments, thoughts, and feelings, re-shape them during a speech, and finally
build knowledge out of all information provided in the conversation. Speakers
share a common interest to discuss. It is expected that each speaker's reply
includes duplicated forms of words from previous speakers. However, linguistic
adaptation is observed and evolves in a more complex path than just
transferring slightly modified versions of common concepts. A conversation
aiming a benefit at the end shows an emergent cooperation inducing the
adaptation. Not only cooperation, but also competition drives the adaptation or
an opposite scenario and one can capture the dynamic process by tracking how
the concepts are linguistically linked. To uncover salient complex dynamic
events in verbal communications, we attempt to discover self-organized
linguistic relations hidden in a conversation with explicitly stated winners
and losers. We examine open access data of the United States Supreme Court. Our
understanding is crucial in big data research to guide how transition states in
opinion mining and decision-making should be modeled and how this required
knowledge to guide the model should be pinpointed, by filtering large amount of
data.
