·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Replit is no longer working with Anthropic, CEO says6h◆How Itaú is building generative AI for its entire customer base8h◆Roundtables: A Conversation With the Creator of AI-Designed Viruses9h◆India becomes the latest country to report rogue AI agent activity10h◆An Nvidia insider-turned-VC on why robotics hasn’t hit its ChatGPT moment, yet11h◆Physical AI is more than robots, says the Nvidia veteran now investing in it11h◆Elorian’s first AI model wants to reason with images, not just about them11h◆Jev is Vercel’s fastest-adopted AI model ever12h◆A week with RTX Spark: everything we’ve learned using Nvidia’s new laptops12h◆Mark Wahlberg on using AI in film ‘the right way’ and being open to an AI clone carrying on his legacy13h◆Meta’s new terms may allow it to train AI on news sites for free13h◆Veritas expands its anti-counterfeit chip to jewelry, trophies, and fragrance bottles13h◆Replit’s CEO says the future of work is the ‘self-driving company’15h◆Optimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets23h◆Judging the Judges: Confidence and Disagreement in Rubrics as Rewards23h◆Content associations with answer selection and engagement in supply chain community question answering23h◆ORACLE-SWE: Quantifying the Contribution of Critical Information Signals on SWE Agents23h◆REKI: Registering Knowledge in VLAs23h◆Multimodal Remote Inference23h◆HyCo: Hybrid Drafting for Cost-Efficient Mobile-Cloud Speculative Decoding23h◆Replit is no longer working with Anthropic, CEO says6h◆How Itaú is building generative AI for its entire customer base8h◆Roundtables: A Conversation With the Creator of AI-Designed Viruses9h◆India becomes the latest country to report rogue AI agent activity10h◆An Nvidia insider-turned-VC on why robotics hasn’t hit its ChatGPT moment, yet11h◆Physical AI is more than robots, says the Nvidia veteran now investing in it11h◆Elorian’s first AI model wants to reason with images, not just about them11h◆Jev is Vercel’s fastest-adopted AI model ever12h◆A week with RTX Spark: everything we’ve learned using Nvidia’s new laptops12h◆Mark Wahlberg on using AI in film ‘the right way’ and being open to an AI clone carrying on his legacy13h◆Meta’s new terms may allow it to train AI on news sites for free13h◆Veritas expands its anti-counterfeit chip to jewelry, trophies, and fragrance bottles13h◆Replit’s CEO says the future of work is the ‘self-driving company’15h◆Optimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets23h◆Judging the Judges: Confidence and Disagreement in Rubrics as Rewards23h◆Content associations with answer selection and engagement in supply chain community question answering23h◆ORACLE-SWE: Quantifying the Contribution of Critical Information Signals on SWE Agents23h◆REKI: Registering Knowledge in VLAs23h◆Multimodal Remote Inference23h◆HyCo: Hybrid Drafting for Cost-Efficient Mobile-Cloud Speculative Decoding23h◆
News/LLM-Based vs. Lexicon-Based Sentiment Signals for Tail-Risk Detection in Meme Stocks
arxiv
PublishedJuly 28, 2026 at 4:00 AM

LLM-Based vs. Lexicon-Based Sentiment Signals for Tail-Risk Detection in Meme Stocks

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2607.24072v1 Announce Type: new Abstract: This paper presents an empirical comparison of lexicon-based and Large Language Model (LLM)-based sentiment analysis for extracting market-relevant signals from social media discourse in highly volatile equity markets. Using Reddit data from r/WallStre

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivOptimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets23harxivJudging the Judges: Confidence and Disagreement in Rubrics as Rewards23harxivContent associations with answer selection and engagement in supply chain community question answering23harxivORACLE-SWE: Quantifying the Contribution of Critical Information Signals on SWE Agents23h
The Bubble Brief
MON · WED · FRI

Read AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews