  Domestic Violence against women is now recognized to be a serious and
widespread problem worldwide. Domestic Violence and Abuse is at the root of so
many issues in society and considered as the societal tabooed topic.
Fortunately, with the popularity of social media, social welfare communities
and victim support groups facilitate the victims to share their abusive stories
and allow others to give advice and help victims. Hence, in order to offer the
immediate resources for those needs, the specific messages from the victims
need to be alarmed from other messages. In this paper, we regard intention
mining as a binary classification problem (abuse or advice) with the usecase of
abuse discourse. To address this problem, we extract rich feature sets from the
raw corpus, using psycholinguistic clues and textual features by term-class
interaction method. Machine learning algorithms are used to predict the
accuracy of the classifiers between two different feature sets. Our
experimental results with high classification accuracy give a promising
solution to understand a big social problem through big social media and its
use in serving information needs of various community welfare organizations.
