"Hang in There": Lexical and Visual Analysis to Identify Posts Warranting Empathetic Responses
Mimansa Jaiswal, Sairam Tabibu, Erik Cambria

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.
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMental Health via Writing · Sentiment Analysis and Opinion Mining · Topic Modeling
