  In the past few years, social media has risen as a platform where people
express and share personal incidences about abuse, violence and mental health
issues. There is a need to pinpoint such posts and learn the kind of response
expected. For this purpose, we understand the sentiment that a personal story
elicits on different posts present on different social media sites, on the
topics of abuse or mental health. In this paper, we propose a method supported
by hand-crafted features to judge if the post requires an empathetic response.
The model is trained upon posts from various web-pages and corresponding
comments, on both the captions and the images. We were able to obtain 80%
accuracy in tagging posts requiring empathetic responses.
