Tootfinder

MS-Mix: Unveiling the Power of Mixup for Multimodal Sentiment Analysis
Hongyu Zhu, Lin Chen, Mounim A. El-Yacoubi, Mingsheng Shang
https://arxiv.org/abs/2510.11579 https://

MS-Mix: Unveiling the Power of Mixup for Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) aims to identify and interpret human emotions by integrating information from heterogeneous data sources such as text, video, and audio. While deep learning models have advanced in network architecture design, they remain heavily limited by scarce multimodal annotated data. Although Mixup-based augmentation improves generalization in unimodal tasks, its direct application to MSA introduces critical challenges: random mixing often amplifies label ambiguity and…

SenWave: A Fine-Grained Multi-Language Sentiment Analysis Dataset Sourced from COVID-19 Tweets
Qiang Yang, Xiuying Chen, Changsheng Ma, Rui Yin, Xin Gao, Xiangliang Zhang
https://arxiv.org/abs/2510.08214

SenWave: A Fine-Grained Multi-Language Sentiment Analysis Dataset Sourced from COVID-19 Tweets
The global impact of the COVID-19 pandemic has highlighted the need for a comprehensive understanding of public sentiment and reactions. Despite the availability of numerous public datasets on COVID-19, some reaching volumes of up to 100 billion data points, challenges persist regarding the availability of labeled data and the presence of coarse-grained or inappropriate sentiment labels. In this paper, we introduce SenWave, a novel fine-grained multi-language sentiment analysis dataset specific…

Sentiment Matters: An Analysis of 200 Human-SAV Interactions
Lirui Guo, Michael G. Burke, Wynita M. Griggs
https://arxiv.org/abs/2510.08202 https://arxiv.o…

Sentiment Matters: An Analysis of 200 Human-SAV Interactions
Shared Autonomous Vehicles (SAVs) are likely to become an important part of the transportation system, making effective human-SAV interactions an important area of research. This paper introduces a dataset of 200 human-SAV interactions to further this area of study. We present an open-source human-SAV conversational dataset, comprising both textual data (e.g., 2,136 human-SAV exchanges) and empirical data (e.g., post-interaction survey results on a range of psychological factors). The dataset's…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/8]:
- PABSA: Hybrid Framework for Persian Aspect-Based Sentiment Analysis
Mehrzad Tareh, Aydin Mohandesi, Ebrahim Ansari

Unpacking Discourses on Childbirth and Parenthood in Popular Social Media Platforms Across China, Japan, and South Korea
Zheng Wei, Yunqi Li, Yucheng He, Yuelu Li, Xian Xu, Huamin Qu, Pan Hui, Muzhi Zhou
https://arxiv.org/abs/2510.06788

Unpacking Discourses on Childbirth and Parenthood in Popular Social Media Platforms Across China, Japan, and South Korea
Social media use has been shown to be associated with low fertility desires. However, we know little about the discourses surrounding childbirth and parenthood that people consume online. We analyze 219,127 comments on 668 short videos related to reproduction and parenthood from Douyin and Tiktok in China, South Korea, and Japan, a region famous for its extremely low fertility level, to examine the topics and sentiment expressed online. BERTopic model is used to assist thematic analysis, and a …

GateMABSA: Aspect-Image Gated Fusion for Multimodal Aspect-based Sentiment Analysis
Adamu Lawan, Haruna Yunusa
https://arxiv.org/abs/2509.25037 https://arx…

GateMABSA: Aspect-Image Gated Fusion for Multimodal Aspect-based Sentiment Analysis
Aspect-based Sentiment Analysis (ABSA) has recently advanced into the multimodal domain, where user-generated content often combines text and images. However, existing multimodal ABSA (MABSA) models struggle to filter noisy visual signals, and effectively align aspects with opinion-bearing content across modalities. To address these challenges, we propose GateMABSA, a novel gated multimodal architecture that integrates syntactic, semantic, and fusion-aware mLSTM. Specifically, GateMABSA introdu…

Reading Between the Lines: Scalable User Feedback via Implicit Sentiment in Developer Prompts
Daye Nam, Malgorzata Salawa, Satish Chandra
https://arxiv.org/abs/2509.18361 https:…

FinSentLLM: Multi-LLM and Structured Semantic Signals for Enhanced Financial Sentiment Forecasting
Zijian Zhang, Rong Fu, Yangfan He, Xinze Shen, Yanlong Wang, Xiaojing Du, Haochen You, Jiazhao Shi, Simon Fong
https://arxiv.org/abs/2509.12638

FinSentLLM: Multi-LLM and Structured Semantic Signals for Enhanced Financial Sentiment Forecasting
Financial sentiment analysis (FSA) has attracted significant attention, and recent studies increasingly explore large language models (LLMs) for this field. Yet most work evaluates only classification metrics, leaving unclear whether sentiment signals align with market behavior. We propose FinSentLLM, a lightweight multi-LLM framework that integrates an expert panel of sentiment forecasting LLMs, and structured semantic financial signals via a compact meta-classifier. This design captures exper…

COLE: a Comprehensive Benchmark for French Language Understanding Evaluation
David Beauchemin, Yan Tremblay, Mohamed Amine Youssef, Richard Khoury
https://arxiv.org/abs/2510.05046

COLE: a Comprehensive Benchmark for French Language Understanding Evaluation
To address the need for a more comprehensive evaluation of French Natural Language Understanding (NLU), we introduce COLE, a new benchmark composed of 23 diverse task covering a broad range of NLU capabilities, including sentiment analysis, paraphrase detection, grammatical judgment, and reasoning, with a particular focus on linguistic phenomena relevant to the French language. We benchmark 94 large language models (LLM), providing an extensive analysis of the current state of French NLU. Our r…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/11]:
- KuBERT: Central Kurdish BERT Model and Its Application for Sentiment Analysis
Kozhin muhealddin Awlla, Hadi Veisi, Abdulhady Abas Abdullah

A meta-analysis on the performance of machine-learning based language models for sentiment analysis
Elena Rohde, Jonas Klingwort, Christian Borgs
https://arxiv.org/abs/2509.09728

A meta-analysis on the performance of machine-learning based language models for sentiment analysis
This paper presents a meta-analysis evaluating ML performance in sentiment analysis for Twitter data. The study aims to estimate the average performance, assess heterogeneity between and within studies, and analyze how study characteristics influence model performance. Using PRISMA guidelines, we searched academic databases and selected 195 trials from 20 studies with 12 study features. Overall accuracy, the most reported performance metric, was analyzed using double arcsine transformation and …

Pixels to Prices: Visual Traits, Market Cycles, and the Economics of NFT Valuation
Samiha Tariq
https://arxiv.org/abs/2509.24879 https://arxiv.org/pdf/2509…

Pixels to Prices: Visual Traits, Market Cycles, and the Economics of NFT Valuation
This paper studies how visual traits and market cycles shape prices in NFT markets. Using 94,039 transactions from 26 major generative Ethereum collections, the analysis extracts 196 machine-quantified image features (covering color, composition, palette structure, geometry, texture, and deep learning embeddings), then applies a three-stage filter process to identify stable predictors for hedonic regression. A static mixed-effects model shows that market sentiment and transparent, interpretable…

From Headlines to Holdings: Deep Learning for Smarter Portfolio Decisions
Yun Lin, Jiawei Lou, Jinghe Zhang
https://arxiv.org/abs/2509.24144 https://arxiv.…

From Headlines to Holdings: Deep Learning for Smarter Portfolio Decisions
Deep learning offers new tools for portfolio optimization. We present an end-to-end framework that directly learns portfolio weights by combining Long Short-Term Memory (LSTM) networks to model temporal patterns, Graph Attention Networks (GAT) to capture evolving inter-stock relationships, and sentiment analysis of financial news to reflect market psychology. Unlike prior approaches, our model unifies these elements in a single pipeline that produces daily allocations. It avoids the traditional…

A Comparative Evaluation of Large Language Models for Persian Sentiment Analysis and Emotion Detection in Social Media Texts
Kian Tohidi, Kia Dashtipour, Simone Rebora, Sevda Pourfaramarz
https://arxiv.org/abs/2509.14922

A Comparative Evaluation of Large Language Models for Persian Sentiment Analysis and Emotion Detection in Social Media Texts
This study presents a comprehensive comparative evaluation of four state-of-the-art Large Language Models (LLMs)--Claude 3.7 Sonnet, DeepSeek-V3, Gemini 2.0 Flash, and GPT-4o--for sentiment analysis and emotion detection in Persian social media texts. Comparative analysis among LLMs has witnessed a significant rise in recent years, however, most of these analyses have been conducted on English language tasks, creating gaps in understanding cross-linguistic performance patterns. This research ad…

Crisis Messaging Journeys: Epistemic Struggles over CDC Guidance During COVID-19
Tawfiq Ammari
https://arxiv.org/abs/2509.10906 https://arxiv.org/pdf/2509.…

Crisis Messaging Journeys: Epistemic Struggles over CDC Guidance During COVID-19
This study investigates how the U.S. Centers for Disease Control and Prevention (CDC) communicated COVID-19 guidance on Twitter and how publics responded over two years of the pandemic. Drawing on 275,124 tweets mentioning or addressing @CDCgov, I combine BERTopic modeling, sentiment analysis (VADER), credibility checks (Iffy Index), change point detection (PELT), and survival analysis to trace three phases of discourse: (1) early hoax claims and testing debates, (2) lockdown and mask controver…

Query-Focused Extractive Summarization for Sentiment Explanation
Ahmed Moubtahij, Sylvie Ratt\'e, Yazid Attabi, Maxime Dumas
https://arxiv.org/abs/2509.11989 https://…

Query-Focused Extractive Summarization for Sentiment Explanation
Constructive analysis of feedback from clients often requires determining the cause of their sentiment from a substantial amount of text documents. To assist and improve the productivity of such endeavors, we leverage the task of Query-Focused Summarization (QFS). Models of this task are often impeded by the linguistic dissonance between the query and the source documents. We propose and substantiate a multi-bias framework to help bridge this gap at a domain-agnostic, generic level; we then for…

Crosslisted article(s) found for stat.AP. https://arxiv.org/list/stat.AP/new
[1/1]:
- A meta-analysis on the performance of machine-learning based language models for sentiment analysis
Elena Rohde, Jonas Klingwort, Christian Borgs