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.
@article{arxiv.1903.05210,
title = {"Hang in There": Lexical and Visual Analysis to Identify Posts Warranting Empathetic Responses},
author = {Mimansa Jaiswal and Sairam Tabibu and Erik Cambria},
journal= {arXiv preprint arXiv:1903.05210},
year = {2019}
}