arXiv AI
EmoXpt: Analyzing Emotional Variances in Human Comments and LLM-Generated Responses
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arXiv AI
Aug 19
Potential of ChatGPT in predicting stock market trends based on Twitter Sentiment Analysis
The paper explores whether ChatGPT can predict stock market movements by analyzing Twitter sentiment. Using tweets about Microsoft and Google, the study finds a positive correlation between ChatGPT’s sentiment assessments and the subsequent stock performance of both companies. The results suggest that ChatGPT’s language understanding can translate social media sentiment into useful financial forecasts.
arXiv Computation and Language
Oct 8
Nobody Truly Agrees on Sentiment: Humans, Bespoke Tools, and LLMs Struggle with Social Media Texts
The study evaluates how well three sentiment‑analysis tools (TextBlob, VADER, Twitter‑roBERTa‑base) and three large language models (Qwen3‑32B, GPT‑OSS‑120B, Llama‑4‑Maverick‑17B) agree with six human raters on 100 tweets. Agreement was measured with Cohen’s and Fleiss’ kappa, revealing only fair inter‑human agreement and higher concordance for binary sentiment labels than for three‑class labels. Twitter‑roBERTa‑base achieved the strongest alignment with humans, especially for negative versus non‑negative sentiment, while the LLMs showed substantial agreement among themselves and moderate to substantial alignment with humans, particularly for positive versus non‑positive classifications. The findings emphasize that domain‑specific fine‑tuning and human‑centered evaluation are essential for reliable social media sentiment analysis.
arXiv Computation and Language
Aug 25
Psychologically Potent, Computationally Invisible: LLMs Generate Social-Comparison-Eliciting Posts They Fail to Detect
arXiv:2605.01017v3 Announce Type: replace Abstract: We introduce Xiaohongshu Social Comparison Reader Elicitation (XHS-SCoRE), a reader-grounded benchmark for detecting whether text-only Xiaohongshu...
arXiv AI
Jul 8
Detoxify: A framework for abusive text transformation using LLMs
arXiv:2507. 10177v2 Announce Type: replace-cross Abstract: Although Large Language Models (LLMs) have demonstrated significant advancements in natural language processing tasks, their effectiveness in the classification and transformation of abusive text into non-abusive versions remains an area for exploration.
arXiv AI
Sep 3
EmoStance: Response-Side Affective-Orientation Control for Empathetic Response Generation via Emoji Weak Supervision
The paper introduces EmoStance, a method for controlling the affective orientation of empathetic responses by leveraging weak supervision from emoji distributions. It builds the EmojiDialogue dataset, extending EmpatheticDialogues with emoji votes and confidence scores, and uses a frozen instruction‑tuned LLM steered by continuous prefix embeddings to generate responses that align with the listener’s stance. In blind pairwise evaluations, EmoStance achieves a 62.2% decisive win rate, notably improving contextual specificity and perceived responsiveness compared to baselines.
Hugging Face Trending Papers
Sep 2
EmoStance: Response-Side Affective-Orientation Control for Empathetic Response Generation via Emoji Weak Supervision
The paper introduces EmoStance, a method for controlling the affective orientation of empathetic responses in dialogue systems. It leverages weak supervision from multi‑annotator emoji distributions to create a latent control space that approximates listener stance, and uses a frozen instruction‑tuned LLM steered by continuous prefix embeddings. Evaluation shows a 62.2% decisive win rate over baselines, especially in contextual specificity and perceived responsiveness.