  Arguments, counter-arguments, facts, and evidence obtained via documents
related to previous court cases are of essential need for legal professionals.
Therefore, the process of automatic information extraction from documents
containing legal opinions related to court cases can be considered to be of
significant importance. This study is focused on the identification of
sentences in legal opinion texts which convey different perspectives on a
certain topic or entity. We combined several approaches based on semantic
analysis, open information extraction, and sentiment analysis to achieve our
objective. Then, our methodology was evaluated with the help of human judges.
The outcomes of the evaluation demonstrate that our system is successful in
detecting situations where two sentences deliver different opinions on the same
topic or entity. The proposed methodology can be used to facilitate other
information extraction tasks related to the legal domain. One such task is the
automated detection of counter arguments for a given argument. Another is the
identification of opponent parties in a court case.
