Papers by Soumitra Ghosh

5 papers
CEASE, a Corpus of Emotion Annotated Suicide notes in English (2020.lrec-1)

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Challenge: Existing studies on suicide notes have not explored the topic of emotion detection.
Approach: They develop a fine-grained emotion annotated corpus of suicide notes in English and use it to perform emotion detection on a curated dataset.
Outcome: The proposed model performs emotion detection on a curated dataset of 205 suicide notes in English.
Just a Scratch: Enhancing LLM Capabilities for Self-harm Detection through Intent Differentiation and Emoji Interpretation (2025.acl-long)

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Challenge: Self-harm detection on social media is critical for early intervention and mental health support, yet remains challenging due to the subtle, context-dependent nature of such expressions.
Approach: They propose a framework to distinguish intent through nuanced language–emoji interplay.
Outcome: The proposed framework improves self-harm detection and explanation tasks on three state-of-the-art LLMs.
Standardizing Distress Analysis: Emotion-Driven Distress Identification and Cause Extraction (DICE) in Multimodal Online Posts (2023.emnlp-main)

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Challenge: Existing methods for identifying hate speech have been limited to analyzing textual content.
Approach: They propose a method for distress identification and cause extraction from social media posts using emotional information.
Outcome: The proposed method improves F1 and ROS scores by 1.95% and 3% relative to the best-performing baseline.
COMMA-DEER: COmmon-sense Aware Multimodal Multitask Approach for Detection of Emotion and Emotional Reasoning in Conversations (2022.coling-1)

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Challenge: Mental health is a critical component of the United Nations’ Sustainable Development Goals (SDGs), particularly Goal 3 which aims to provide “good health and well-being”.
Approach: They propose a task of detecting emotional reasoning and accompanying emotions in conversations that is manually annotated at the utterance level.
Outcome: The proposed model achieves 6% accuracy and 4.62% accuracy on the emotion detection task and 3.56% accuracy, and 3.31% F1 on the ER detection task, compared to the existing state-of-the-art model.
EM-PERSONA: EMotion-assisted Deep Neural Framework for PERSONAlity Subtyping from Suicide Notes (2022.coling-1)

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Challenge: Suicide continues to be one of the significant causes of death worldwide . EMotion-assisted personality subtyping is a novel approach to identify personality traits from suicide notes .
Approach: They propose to use a PERSONAlity Detection Framework to identify personality traits from suicide notes and annotate them using a benchmark dataset.
Outcome: The proposed method outperforms baselines on comprehensive evaluation using multiple state-of-the-art systems.

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