# "Hang in There": Lexical and Visual Analysis to Identify Posts Warranting Empathetic Responses

## TL;DR

This paper presents a method combining lexical and visual features to identify social media posts that warrant empathetic responses, achieving 80% accuracy across diverse online platforms.

## Contribution

It introduces a novel approach using handcrafted features to detect posts needing empathy, integrating both text and image analysis.

## Key findings

- Achieved 80% accuracy in identifying posts requiring empathy
- Utilized features from captions and images across multiple social media sites
- Demonstrated effectiveness of combined lexical and visual analysis

## Abstract

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.

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Source: https://tomesphere.com/paper/1903.05210