sentiment-analysis

This paper examines how sentiment arcs in ECB and Fed press conferences predict policy rate changes and inflation expectations, showing that the sequencing of sentiment carries significant policy signals.

semfont is a library that automatically applies typographic styles to text based on word scores for valence, salience, surprise, and certainty to enhance readability.

This paper presents a cross-platform NLP analysis of 17,012 app store reviews for six major generative AI applications, identifying key trust and friction barriers like advertisements, authentication, and pricing, with significant sentiment variations across platforms.

This paper tests the assumption that sentiment tools validated against human labels also predict market signals in financial NLP, finding that benchmark agreement does not determine predictive effectiveness and spam influences volume-based analysis.

This paper critiques optimization-based balancing strategies for multimodal sentiment analysis, showing they fail due to conflating fitting speed with discriminative importance, and proposes a new research agenda focusing on held-out modality valuation.

A website aggregates public sentiment from X (Twitter) about the AI agent Devin developed by Cognition.

The paper presents C3T, a thread-structured temporal model for predicting sentiment shifts in social media conversation trees using counterfactual causal reasoning, and introduces the CaSiRe dataset for causal sentiment reasoning.

The article explores the concept of sentiment halflife in AI models, indicating a potentially significant insight into model longevity and performance degradation.

This paper presents the first application of data science to evaluate the UK Honours system using natural language processing, introducing a novel sentiment analysis algorithm called Minos to assess public opinion on honours recipients.

SocialCrawl is an API designed to help AI agents access live social and web data from various platforms. PRISM, a new set of endpoints, merges search results from multiple sources to support workflows like brand monitoring and sentiment analysis.

This paper conducts a multi-method computational analysis of Telegram discourse on the Israel-Palestine conflict, revealing that pro-Israel channels use a neutral, report-style tone while pro-Palestine channels exhibit more negative sentiment and framing.

This paper introduces a label-free method to find a valence axis from nine emotion examples that transfers across text, vision, audio, and brain modalities, achieving competitive sentiment classification with minimal labels.

This paper explores ChatGPT's potential to predict stock market movements by analyzing sentiment from Twitter posts, revealing a positive correlation with subsequent stock performance for Microsoft and Google.

This paper presents an LLM-based pipeline for analyzing media bias and framing in online news, tested on Albanian articles with moderate agreement in annotations compared to automated methods.

This paper presents an empirical study on sentiment classifier behavior with sarcastic and AI-paraphrased social text, revealing lower confidence on sarcasm, higher accuracy on AI paraphrases, and an abstention method that improves performance by handling low-confidence inputs.

This paper introduces ViTOED, a new dataset for target-oriented emotion detection on Vietnamese social media texts, containing nearly 11,000 comments with manually annotated opinion quadruples. It evaluates Vietnamese pre-trained language models using structured sentiment graphs, highlighting challenges in span detection and relation extraction.

This paper proposes MIDAS, a unified framework for incomplete multimodal sentiment analysis that uses mutual information disentanglement and uncertainty-aware fusion to robustly represent and integrate modalities under missing-data conditions.

This paper compares RoBERTa-based sentiment analysis with an LLM-based multi-dimensional framing analysis on political news articles, finding that traditional SA suffers from 'neutral collapse' and that LLM-based approaches better capture bias, sensationalism, and framing for social science research.

This paper describes a two-stage vision-language adaptation system for Nepali meme classification, using Qwen3-VL-8B-Instruct with LoRA fine-tuning and contrastive learning. The system achieved 2nd place in hate speech detection and 4th in sentiment analysis at the CHiPSAL 2026 shared task.

The paper proposes a Regime-Aware Multi-Modal Learning (RAML) method for Bitcoin price direction prediction that adaptively fuses social sentiment and technical features based on market volatility. Evaluated on hourly data from July 2024 to September 2025, RAML achieves moderate improvements over static fusion baselines.