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.
By Ummara Mumtaz, Summaya Mumtaz
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.
By Himarsha R. Jayanetti, Sivakanesan Dhanushkanda, Shuai Hao, Michael L. Nelson, Michele C. Weigle
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...
By Hua Zhao, Jiapei Gu, Michelle Mingyue Gu
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.
By Rohitash Chandra, Jiyong Choi, Jayesh Sonawane
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.
By Ziyuan Jin, Yuxuan Ge, Zheng Tian
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.
arXiv:2610.28337v1 Announce Type: cross
Abstract: Emoji are widely treated as compact affective tokens whose meaning is summarised by a single sentiment score. We argue that this picture is incomplet...
By Rohan James, Ioannis Christofilogiannis, Yuuv Jauhari, Shreya Shah, Gauri Pethkar
arXiv:2610.08829v1 Announce Type: new
Abstract: Jev offers an alternative interface for language understanding: given an input and predefined questions, it returns probabilistic decisions rather than...
By Yazhou Zhang, Junhao Yu
arXiv:2610.20548v1 Announce Type: new
Abstract: Understanding the social intelligence of large language models (LLMs) is increasingly important for applications involving human-AI interaction and mul...
By Ali Zamani, Soroush Esfahanian, Erfan Shahabi, Mohammad Taher Pilehvar, Azadeh Shakery, Yadollah Yaghoobzadeh
arXiv:2607. 11890v1 Announce Type: cross Abstract: Open-ended surveys offer valuable insights, but they are notoriously difficult to analyze at scale.
By Abdullah Akinde, Mariam Akinde, Rasheedat Emiola, Ahmed Akinsola
arXiv:2608. 15338v1 Announce Type: cross Abstract: Sentiment classifiers are increasingly applied to social media content that is either sarcastic or AI-generated --- two distributional regimes where standard evaluations offer little guidance.
By Shresth Shroff
Understanding the social intelligence of large language models (LLMs) is increasingly important for applications involving human-AI interaction and multi-agent environments. Social intelligence compri...