Tootfinder

Causal Prompting for Implicit Sentiment Analysis with Large Language Models
Jing Ren, Wenhao Zhou, Bowen Li, Mujie Liu, Nguyen Linh Dan Le, Jiade Cen, Liping Chen, Ziqi Xu, Xiwei Xu, Xiaodong Li
https://arxiv.org/abs/2507.00389

Causal Prompting for Implicit Sentiment Analysis with Large Language Models
Implicit Sentiment Analysis (ISA) aims to infer sentiment that is implied rather than explicitly stated, requiring models to perform deeper reasoning over subtle contextual cues. While recent prompting-based methods using Large Language Models (LLMs) have shown promise in ISA, they often rely on majority voting over chain-of-thought (CoT) reasoning paths without evaluating their causal validity, making them susceptible to internal biases and spurious correlations. To address this challenge, we …

Hybrid CNN-Mamba Enhancement Network for Robust Multimodal Sentiment Analysis
Xiang Li, Xianfu Cheng, Xiaoming Zhang, Zhoujun Li
https://arxiv.org/abs/2507.23444 https://…

Hybrid CNN-Mamba Enhancement Network for Robust Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) with missing modalities has recently attracted increasing attention. Although existing research mainly focuses on designing complex model architectures to handle incomplete data, it still faces significant challenges in effectively aligning and fusing multimodal information. In this paper, we propose a novel framework called the Hybrid CNN-Mamba Enhancement Network (HCMEN) for robust multimodal sentiment analysis under missing modality conditions. HCMEN is de…

Shifting Narratives: A Longitudinal Analysis of Media Trends and Public Attitudes on Homelessness
Akshay Irudayaraj (Brown University), Nathan Ye (University of California--Berkeley), Yash Chainani (University of California--Berkeley)
https://arxiv.org/abs/2506.21794

Shifting Narratives: A Longitudinal Analysis of Media Trends and Public Attitudes on Homelessness
Within the field of media framing, homelessness has been a historically under-researched topic. Framing theory states that the media's method of presenting information plays a pivotal role in controlling public sentiment toward a topic. The sentiment held towards homeless individuals influences their ability to access jobs, housing, and resources as a result of discrimination. This study analyzes the topic and sentiment trends in related media articles to validate framing theory within the scop…

Quantum-Cognitive Tunnelling Neural Networks for Military-Civilian Vehicle Classification and Sentiment Analysis
Milan Maksimovic, Anna Bohdanets, Immaculate Motsi-Omoijiade, Guido Governatori, Ivan S. Maksymov
https://arxiv.org/abs/2507.18645

Quantum-Cognitive Tunnelling Neural Networks for Military-Civilian Vehicle Classification and Sentiment Analysis
Prior work has demonstrated that incorporating well-known quantum tunnelling (QT) probability into neural network models effectively captures important nuances of human perception, particularly in the recognition of ambiguous objects and sentiment analysis. In this paper, we employ novel QT-based neural networks and assess their effectiveness in distinguishing customised CIFAR-format images of military and civilian vehicles, as well as sentiment, using a proprietary military-specific vocabulary…

Beyond the Black Box: Interpretability of LLMs in Finance
Hariom Tatsat (Barclays), Ariye Shater (Barclays)
https://arxiv.org/abs/2505.24650 https://

Beyond the Black Box: Interpretability of LLMs in Finance
Large Language Models (LLMs) exhibit remarkable capabilities across a spectrum of tasks in financial services, including report generation, chatbots, sentiment analysis, regulatory compliance, investment advisory, financial knowledge retrieval, and summarization. However, their intrinsic complexity and lack of transparency pose significant challenges, especially in the highly regulated financial sector, where interpretability, fairness, and accountability are critical. As far as we are aware, t…

Reasoning-Intensive Regression
Diane Tchuindjo, Omar Khattab
https://arxiv.org/abs/2508.21762 https://arxiv.org/pdf/2508.21762

Reasoning-Intensive Regression
AI researchers and practitioners increasingly apply large language models (LLMs) to what we call reasoning-intensive regression (RiR), i.e. deducing subtle numerical properties from text. Unlike standard language regression tasks, e.g. for sentiment or similarity, RiR often appears instead in ad-hoc problems like rubric-based scoring or domain-specific retrieval, where much deeper analysis of text is required while only limited task-specific training data and computation are available. We cast …

A Financial Brain Scan of the LLM
Hui Chen, Antoine Didisheim, Luciano Somoza, Hanqing Tian
https://arxiv.org/abs/2508.21285 https://arxiv.org/pdf/2508.212…

A Financial Brain Scan of the LLM
Emerging techniques in computer science make it possible to "brain scan" large language models (LLMs), identify the plain-English concepts that guide their reasoning, and steer them while holding other factors constant. We show that this approach can map LLM-generated economic forecasts to concepts such as sentiment, technical analysis, and timing, and compute their relative importance without reducing performance. We also show that models can be steered to be more or less risk-averse, optimist…

This https://arxiv.org/abs/2505.23018 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMM_…

EmotionTalk: An Interactive Chinese Multimodal Emotion Dataset With Rich Annotations
In recent years, emotion recognition plays a critical role in applications such as human-computer interaction, mental health monitoring, and sentiment analysis. While datasets for emotion analysis in languages such as English have proliferated, there remains a pressing need for high-quality, comprehensive datasets tailored to the unique linguistic, cultural, and multimodal characteristics of Chinese. In this work, we propose \textbf{EmotionTalk}, an interactive Chinese multimodal emotion datase…

Opportunities and Challenges of Integrating ChatGPT in Education: Sentiment Analysis and Topic Modeling
Surat Teerakapibal, Poompak Kusawat
https://arxiv.org/abs/2508.16966 http…

Opportunities and Challenges of Integrating ChatGPT in Education: Sentiment Analysis and Topic Modeling
Since its recent debut, ChatGPT has become a global sensation and significantly impacted the field of education. Both educational researchers and practitioners have identified opportunities as well as risks associated with the use of this novel tool in educational settings. Despite the ongoing debate, there is still no research exploring occupational differences in the perception of ChatGPT in education. In this paper, we analyzed Twitter data using topic modeling and sentiment analysis to inve…

I think this is a great summary of “vibe coding.” 🫠 (The whole thread is worth a read.) /cc @…
https://mastodon.social/@jcoglan/114752172786629305…

A Retail-Corpus for Aspect-Based Sentiment Analysis with Large Language Models
Oleg Silcenco, Marcos R. Machad, Wallace C. Ugulino, Daniel Braun
https://arxiv.org/abs/2508.17994

A Retail-Corpus for Aspect-Based Sentiment Analysis with Large Language Models
Aspect-based sentiment analysis enhances sentiment detection by associating it with specific aspects, offering deeper insights than traditional sentiment analysis. This study introduces a manually annotated dataset of 10,814 multilingual customer reviews covering brick-and-mortar retail stores, labeled with eight aspect categories and their sentiment. Using this dataset, the performance of GPT-4 and LLaMA-3 in aspect based sentiment analysis is evaluated to establish a baseline for the newly in…

Research Challenges in Relational Database Management Systems for LLM Queries
Kerem Akillioglu, Anurag Chakraborty, Sairaj Voruganti, M. Tamer \"Ozsu
https://arxiv.org/abs/2508.20912

Research Challenges in Relational Database Management Systems for LLM Queries
Large language models (LLMs) have become essential for applications such as text summarization, sentiment analysis, and automated question-answering. Recently, LLMs have also been integrated into relational database management systems to enhance querying and support advanced data processing. Companies such as Amazon, Databricks, Google, and Snowflake offer LLM invocation directly within SQL, denoted as LLM queries, to boost data insights. However, open-source solutions currently have limited fu…

Speech Emotion Recognition via Entropy-Aware Score Selection
ChenYi Chua, JunKai Wong, Chengxin Chen, Xiaoxiao Miao
https://arxiv.org/abs/2508.20796 https://

Speech Emotion Recognition via Entropy-Aware Score Selection
In this paper, we propose a multimodal framework for speech emotion recognition that leverages entropy-aware score selection to combine speech and textual predictions. The proposed method integrates a primary pipeline that consists of an acoustic model based on wav2vec2.0 and a secondary pipeline that consists of a sentiment analysis model using RoBERTa-XLM, with transcriptions generated via Whisper-large-v3. We propose a late score fusion approach based on entropy and varentropy thresholds to …

Cross-Subreddit Behavior as Open-Source Indicators of Coordinated Influence: A Case Study of r/Sino & r/China
Manon Pilaud, Ian McCulloh
https://arxiv.org/abs/2507.16857 htt…

Cross-Subreddit Behavior as Open-Source Indicators of Coordinated Influence: A Case Study of r/Sino & r/China
This study investigates potential indicators of coordinated influence activity among users participating in both r/Sino and r/China, two ideologically divergent Reddit communities focused on Chinese political discourse. Topic modeling and sentiment analysis are applied to all posts and comments authored by dual-subreddit users to construct a user-topic sentiment matrix. Individual sentiment patterns are compared to global topic baselines derived from the broader r/Sino and r/China populations. …

HARLF: Hierarchical Reinforcement Learning and Lightweight LLM-Driven Sentiment Integration for Financial Portfolio Optimization
Benjamin Coriat, Eric Benhamou
https://arxiv.org/abs/2507.18560

HARLF: Hierarchical Reinforcement Learning and Lightweight LLM-Driven Sentiment Integration for Financial Portfolio Optimization
This paper presents a novel hierarchical framework for portfolio optimization, integrating lightweight Large Language Models (LLMs) with Deep Reinforcement Learning (DRL) to combine sentiment signals from financial news with traditional market indicators. Our three-tier architecture employs base RL agents to process hybrid data, meta-agents to aggregate their decisions, and a super-agent to merge decisions based on market data and sentiment analysis. Evaluated on data from 2018 to 2024, after t…

Towards Trustworthy Sentiment Analysis in Software Engineering: Dataset Characteristics and Tool Selection
Martin Obaidi, Marc Herrmann, Jil Kl\"under, Kurt Schneider
https://arxiv.org/abs/2507.02137

Towards Trustworthy Sentiment Analysis in Software Engineering: Dataset Characteristics and Tool Selection
Software development relies heavily on text-based communication, making sentiment analysis a valuable tool for understanding team dynamics and supporting trustworthy AI-driven analytics in requirements engineering. However, existing sentiment analysis tools often perform inconsistently across datasets from different platforms, due to variations in communication style and content. In this study, we analyze linguistic and statistical features of 10 developer communication datasets from five pla…

FinDPO: Financial Sentiment Analysis for Algorithmic Trading through Preference Optimization of LLMs
Giorgos Iacovides, Wuyang Zhou, Danilo Mandic
https://arxiv.org/abs/2507.18417

FinDPO: Financial Sentiment Analysis for Algorithmic Trading through Preference Optimization of LLMs
Opinions expressed in online finance-related textual data are having an increasingly profound impact on trading decisions and market movements. This trend highlights the vital role of sentiment analysis as a tool for quantifying the nature and strength of such opinions. With the rapid development of Generative AI (GenAI), supervised fine-tuned (SFT) large language models (LLMs) have become the de facto standard for financial sentiment analysis. However, the SFT paradigm can lead to memorization…

SentiMM: A Multimodal Multi-Agent Framework for Sentiment Analysis in Social Media
Xilai Xu, Zilin Zhao, Chengye Song, Zining Wang, Jinhe Qiang, Jiongrui Yan, Yuhuai Lin
https://arxiv.org/abs/2508.18108

SentiMM: A Multimodal Multi-Agent Framework for Sentiment Analysis in Social Media
With the increasing prevalence of multimodal content on social media, sentiment analysis faces significant challenges in effectively processing heterogeneous data and recognizing multi-label emotions. Existing methods often lack effective cross-modal fusion and external knowledge integration. We propose SentiMM, a novel multi-agent framework designed to systematically address these challenges. SentiMM processes text and visual inputs through specialized agents, fuses multimodal features, enrich…

Learning Explainable Stock Predictions with Tweets Using Mixture of Experts
Wenyan Xu, Dawei Xiang, Rundong Wang, Yonghong Hu, Liang Zhang, Jiayu Chen, Zhonghua Lu
https://arxiv.org/abs/2507.20535

Learning Explainable Stock Predictions with Tweets Using Mixture of Experts
Stock price movements are influenced by many factors, and alongside historical price data, tex-tual information is a key source. Public news and social media offer valuable insights into market sentiment and emerging events. These sources are fast-paced, diverse, and significantly impact future stock trends. Recently, LLMs have enhanced financial analysis, but prompt-based methods still have limitations, such as input length restrictions and difficulties in predicting sequences of varying lengt…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/4]:
- CorMulT: A Semi-supervised Modality Correlation-aware Multimodal Transformer for Sentiment Analysis
Yangmin Li, Ruiqi Zhu, Wengen Li

'A Little Bubble of Friends': An Analysis of LGBTQ Pandemic Experiences Using Reddit Data
Dhruvee Birla, Nazia Akhtar
https://arxiv.org/abs/2507.15033

'A Little Bubble of Friends': An Analysis of LGBTQ+ Pandemic Experiences Using Reddit Data
Social media was one of the most popular forms of communication among young people with digital access during the pandemic. Consequently, crucial debates and discussions about the pandemic crisis have also developed on social media platforms, making them a great primary source to study the experiences of specific groups and communities during the pandemic. This study involved research using LDA topic modeling and sentiment analysis on data obtained from the social media platform Reddit to under…

Comparative Firm-Level Analysis of ESG and Sentiment Effects on Stock Market Performance: Evidence from 16 Companies Using Retrofitted Sentiment Embeddings
Sangdeok Lee
https://arxiv.org/abs/2507.11485

Comparative Firm-Level Analysis of ESG and Sentiment Effects on Stock Market Performance: Evidence from 16 Companies Using Retrofitted Sentiment Embeddings
This study investigates how emotion-specific sentiment embedded in financial news headlines interacts with firm-level ESG (Environmental, Social, and Governance) ratings to influence stock return behavior. Using data from 16 firms and integrating retrofitted word embeddings with ESG metrics, the analysis evaluates three hypotheses: (1) emotion-specific sentiment independently predicts stock returns; (2) its moderating effect varies across ESG dimensions; and (3) positive (negative) sentiment am…

Continuous sentiment scores for literary and multilingual contexts
Laurits Lyngbaek, Pascale Feldkamp, Yuri Bizzoni, Kristoffer Nielbo, Kenneth Enevoldsen
https://arxiv.org/abs/2508.14620

Continuous sentiment scores for literary and multilingual contexts
Sentiment Analysis is widely used to quantify sentiment in text, but its application to literary texts poses unique challenges due to figurative language, stylistic ambiguity, as well as sentiment evocation strategies. Traditional dictionary-based tools often underperform, especially for low-resource languages, and transformer models, while promising, typically output coarse categorical labels that limit fine-grained analysis. We introduce a novel continuous sentiment scoring method based on co…

Leveraging Multi-Source Textural UGC for Neighbourhood Housing Quality Assessment: A GPT-Enhanced Framework
Qiyuan Hong, Huimin Zhao, Ying Long
https://arxiv.org/abs/2508.16657 …

Leveraging Multi-Source Textural UGC for Neighbourhood Housing Quality Assessment: A GPT-Enhanced Framework
This study leverages GPT-4o to assess neighbourhood housing quality using multi-source textural user-generated content (UGC) from Dianping, Weibo, and the Government Message Board. The analysis involves filtering relevant texts, extracting structured evaluation units, and conducting sentiment scoring. A refined housing quality assessment system with 46 indicators across 11 categories was developed, highlighting an objective-subjective method gap and platform-specific differences in focus. GPT-4…

Crosslisted article(s) found for cs.IR. https://arxiv.org/list/cs.IR/new
[1/1]:
- Contextual Attention-Based Multimodal Fusion of LLM and CNN for Sentiment Analysis
Meriem Zerkouk, Miloud Mihoubi, Belkacem Chikhaoui

Sentiment Analysis in Learning Management Systems Understanding Student Feedback at Scale
Mohammed Almutairi
https://arxiv.org/abs/2506.05490 https://

Sentiment Analysis in Learning Management Systems Understanding Student Feedback at Scale
During the wake of the Covid-19 pandemic, the educational paradigm has experienced a major change from in person learning traditional to online platforms. The change of learning convention has impacted the teacher-student especially in non-verbal communication. The absent of non-verbal communication has led to a reliance on verbal feedback which diminished the efficacy of the educational experience. This paper explores the integration of sentiment analysis into learning management systems (LMS)…

AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis
S M Rafiuddin, Sadia Kamal, Mohammed Rakib, Arunkumar Bagavathi, Atriya Sen
https://arxiv.org/abs/2507.12695

AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis
We introduce AdaptiSent, a new framework for Multimodal Aspect-Based Sentiment Analysis (MABSA) that uses adaptive cross-modal attention mechanisms to improve sentiment classification and aspect term extraction from both text and images. Our model integrates dynamic modality weighting and context-adaptive attention, enhancing the extraction of sentiment and aspect-related information by focusing on how textual cues and visual context interact. We tested our approach against several baselines, i…

This https://arxiv.org/abs/2504.15448 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Visualizing Public Opinion on X: A Real-Time Sentiment Dashboard Using VADER and DistilBERT
In the age of social media, understanding public sentiment toward major corporations is crucial for investors, policymakers, and researchers. This paper presents a comprehensive sentiment analysis system tailored for corporate reputation monitoring, combining Natural Language Processing (NLP) and machine learning techniques to accurately interpret public opinion in real time. The methodology integrates a hybrid sentiment detection framework leveraging both rule-based models (VADER) and transfor…

To Trade or Not to Trade: An Agentic Approach to Estimating Market Risk Improves Trading Decisions
Dimitrios Emmanoulopoulos, Ollie Olby, Justin Lyon, Namid R. Stillman
https://arxiv.org/abs/2507.08584

To Trade or Not to Trade: An Agentic Approach to Estimating Market Risk Improves Trading Decisions
Large language models (LLMs) are increasingly deployed in agentic frameworks, in which prompts trigger complex tool-based analysis in pursuit of a goal. While these frameworks have shown promise across multiple domains including in finance, they typically lack a principled model-building step, relying instead on sentiment- or trend-based analysis. We address this gap by developing an agentic system that uses LLMs to iteratively discover stochastic differential equations for financial time serie…

Exploring Public Perceptions of Generative AI in Libraries: A Social Media Analysis of X Discussions
Yuan Li, Teja Mandaloju, Haihua Chen
https://arxiv.org/abs/2507.07047

Exploring Public Perceptions of Generative AI in Libraries: A Social Media Analysis of X Discussions
This study investigates public perceptions of generative artificial intelligence (GenAI) in libraries through a large-scale analysis of posts on X (formerly Twitter). Using a mixed-method approach that combines temporal trend analysis, sentiment classification, and social network analysis, this paper explores how public discourse around GenAI and libraries has evolved over time, the emotional tones that dominate the conversation, and the key users or organizations driving engagement. The findin…

Cross-lingual Few-shot Learning for Persian Sentiment Analysis with Incremental Adaptation
Farideh Majidi, Ziaeddin Beheshtifard
https://arxiv.org/abs/2507.11634

Cross-lingual Few-shot Learning for Persian Sentiment Analysis with Incremental Adaptation
This research examines cross-lingual sentiment analysis using few-shot learning and incremental learning methods in Persian. The main objective is to develop a model capable of performing sentiment analysis in Persian using limited data, while getting prior knowledge from high-resource languages. To achieve this, three pre-trained multilingual models (XLM-RoBERTa, mDeBERTa, and DistilBERT) were employed, which were fine-tuned using few-shot and incremental learning approaches on small samples o…

Unpacking Generative AI in Education: Computational Modeling of Teacher and Student Perspectives in Social Media Discourse
Paulina DeVito, Akhil Vallala, Sean Mcmahon, Yaroslav Hinda, Benjamin Thaw, Hanqi Zhuang, Hari Kalva
https://arxiv.org/abs/2506.16412

Unpacking Generative AI in Education: Computational Modeling of Teacher and Student Perspectives in Social Media Discourse
Generative AI (GAI) technologies are quickly reshaping the educational landscape. As adoption accelerates, understanding how students and educators perceive these tools is essential. This study presents one of the most comprehensive analyses to date of stakeholder discourse dynamics on GAI in education using social media data. Our dataset includes 1,199 Reddit posts and 13,959 corresponding top-level comments. We apply sentiment analysis, topic modeling, and author classification. To support th…

Towards Multimodal Sentiment Analysis via Contrastive Cross-modal Retrieval Augmentation and Hierachical Prompts
Xianbing Zhao, Shengzun Yang, Buzhou Tang, Ronghuan Jiang
https://arxiv.org/abs/2508.07666

Towards Multimodal Sentiment Analysis via Contrastive Cross-modal Retrieval Augmentation and Hierachical Prompts
Multimodal sentiment analysis is a fundamental problem in the field of affective computing. Although significant progress has been made in cross-modal interaction, it remains a challenge due to the insufficient reference context in cross-modal interactions. Current cross-modal approaches primarily focus on leveraging modality-level reference context within a individual sample for cross-modal feature enhancement, neglecting the potential cross-sample relationships that can serve as sample-level …

CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis
Jianfei Li, Kevin Kam Fung Yuen
https://arxiv.org/abs/2507.14022

CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis
This study proposes the Cognitive Pairwise Comparison Classification Model Selection (CPC-CMS) framework for document-level sentiment analysis. The CPC, based on expert knowledge judgment, is used to calculate the weights of evaluation criteria, including accuracy, precision, recall, F1-score, specificity, Matthews Correlation Coefficient (MCC), Cohen's Kappa (Kappa), and efficiency. Naive Bayes, Linear Support Vector Classification (LSVC), Random Forest, Logistic Regression, Extreme Gradient B…

Reference Points in LLM Sentiment Analysis: The Role of Structured Context
Junichiro Niimi
https://arxiv.org/abs/2508.11454 https://arxiv.org/pdf/2508.1145…

Reference Points in LLM Sentiment Analysis: The Role of Structured Context
Large language models (LLMs) are now widely used across many fields, including marketing research. Sentiment analysis, in particular, helps firms understand consumer preferences. While most NLP studies classify sentiment from review text alone, marketing theories, such as prospect theory and expectation--disconfirmation theory, point out that customer evaluations are shaped not only by the actual experience but also by additional reference points. This study therefore investigates how the conte…

Sociotechnical Imaginaries of ChatGPT in Higher Education: The Evolving Media Discourse
Yinan Sun, Ali Unlu, Aditya Johri
https://arxiv.org/abs/2508.14692 https://

Sociotechnical Imaginaries of ChatGPT in Higher Education: The Evolving Media Discourse
This study investigates how U.S. news media framed the use of ChatGPT in higher education from November 2022 to October 2024. Employing Framing Theory and combining temporal and sentiment analysis of 198 news articles, we trace the evolving narratives surrounding generative AI. We found that the media discourse largely centered on institutional responses; policy changes and teaching practices showed the most consistent presence and positive sentiment over time. Conversely, coverage of topics su…

UniSLU: Unified Spoken Language Understanding from Heterogeneous Cross-Task Datasets
Zhichao Sheng, Shilin Zhou, Chen Gong, Zhenghua Li
https://arxiv.org/abs/2507.12951

UniSLU: Unified Spoken Language Understanding from Heterogeneous Cross-Task Datasets
Spoken Language Understanding (SLU) plays a crucial role in speech-centric multimedia applications, enabling machines to comprehend spoken language in scenarios such as meetings, interviews, and customer service interactions. SLU encompasses multiple tasks, including Automatic Speech Recognition (ASR), spoken Named Entity Recognition (NER), and spoken Sentiment Analysis (SA). However, existing methods often rely on separate model architectures for individual tasks such as spoken NER and SA, whi…

The Promise of Large Language Models in Digital Health: Evidence from Sentiment Analysis in Online Health Communities
Xiancheng Li, Georgios D. Karampatakis, Helen E. Wood, Chris J. Griffiths, Borislava Mihaylova, Neil S. Coulson, Alessio Pasinato, Pietro Panzarasa, Marco Viviani, Anna De Simoni
https://arxiv.org/abs/2508.14032

The Promise of Large Language Models in Digital Health: Evidence from Sentiment Analysis in Online Health Communities
Digital health analytics face critical challenges nowadays. The sophisticated analysis of patient-generated health content, which contains complex emotional and medical contexts, requires scarce domain expertise, while traditional ML approaches are constrained by data shortage and privacy limitations in healthcare settings. Online Health Communities (OHCs) exemplify these challenges with mixed-sentiment posts, clinical terminology, and implicit emotional expressions that demand specialised know…

A Midsummer Meme's Dream: Investigating Market Manipulations in the Meme Coin Ecosystem
Alberto Maria Mongardini, Alessandro Mei
https://arxiv.org/abs/2507.01963

A Midsummer Meme's Dream: Investigating Market Manipulations in the Meme Coin Ecosystem
From viral jokes to a billion-dollar phenomenon, meme coins have become one of the most popular segments in cryptocurrency markets. Unlike utility-focused crypto assets like Bitcoin or Ethereum, meme coins derive value primarily from community sentiment, making them vulnerable to manipulation. This study presents a cross-chain analysis of the meme coin ecosystem, examining 34,988 tokens across Ethereum, BNB Smart Chain, Solana, and Base. We characterize the tokenomics of meme coins and track th…

Do Ethical AI Principles Matter to Users? A Large-Scale Analysis of User Sentiment and Satisfaction
Stefan Pasch, Min Chul Cha
https://arxiv.org/abs/2508.05913 https://

Do Ethical AI Principles Matter to Users? A Large-Scale Analysis of User Sentiment and Satisfaction
As AI systems become increasingly embedded in organizational workflows and consumer applications, ethical principles such as fairness, transparency, and robustness have been widely endorsed in policy and industry guidelines. However, there is still scarce empirical evidence on whether these principles are recognized, valued, or impactful from the perspective of users. This study investigates the link between ethical AI and user satisfaction by analyzing over 100,000 user reviews of AI products …

Connecting the Unconnected -- Sentiment Analysis of Field Survey of Internet Connectivity in Emerging Economies
Dibakar Das, Barath S Narayan, Aarna Bhammar, Jyotsna Bapat
https://arxiv.org/abs/2507.06827

Connecting the Unconnected -- Sentiment Analysis of Field Survey of Internet Connectivity in Emerging Economies
Internet has significantly improved the quality of citizens across the world. Though the internet coverage is quite high, 40% of global population do not have access to broadband internet. This paper presents an analysis of a field survey of population in some areas of Kathmandu, Nepal, an emerging economy. This survey was triggered by intermittent severe congestion of internet in certain areas of the city. People from three different areas were asked about their present experience of internet …

Temporal Dynamics of Emotions in Italian Online Soccer Fandoms
Salvatore Citraro, Giovanni Mauro, Emanuele Ferragina
https://arxiv.org/abs/2506.11934 https…

Temporal Dynamics of Emotions in Italian Online Soccer Fandoms
This study investigates the emotional dynamics of Italian soccer fandoms through computational analysis of user-generated content from official Instagram accounts of 83 teams across Serie A, Serie B, and Lega Pro during the 2023-24 season. By applying sentiment analysis to fan comments, we extract temporal emotional patterns and identify distinct clusters of fan bases with similar preseason expectations. Drawing from complex systems theory, we characterize joy as displaying anti-bursty temporal…

FinMultiTime: A Four-Modal Bilingual Dataset for Financial Time-Series Analysis
Wenyan Xu, Dawei Xiang, Yue Liu, Xiyu Wang, Yanxiang Ma, Liang Zhang, Chang Xu, Jiaheng Zhang
https://arxiv.org/abs/2506.05019

FinMultiTime: A Four-Modal Bilingual Dataset for Financial Time-Series Analysis
Pure time series forecasting tasks typically focus exclusively on numerical features; however, real-world financial decision-making demands the comparison and analysis of heterogeneous sources of information. Recent advances in deep learning and large scale language models (LLMs) have made significant strides in capturing sentiment and other qualitative signals, thereby enhancing the accuracy of financial time series predictions. Despite these advances, most existing datasets consist solely of …

Graph-based Interaction Augmentation Network for Robust Multimodal Sentiment Analysis
Hu Zhangfeng, Shi mengxin
https://arxiv.org/abs/2508.01168 https://ar…

Graph-based Interaction Augmentation Network for Robust Multimodal Sentiment Analysis
The inevitable modality imperfection in real-world scenarios poses significant challenges for Multimodal Sentiment Analysis (MSA). While existing methods tailor reconstruction or joint representation learning strategies to restore missing semantics, they often overlook complex dependencies within and across modalities. Consequently, they fail to fully leverage available modalities to capture complementary semantics. To this end, this paper proposes a novel graph-based framework to exploit both …

FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models
Xuan Xu, Fufang Wen, Beilin Chu, Zhibing Fu, Qinhong Lin, Jiaqi Liu, Binjie Fei, Zhongliang Yang, Linna Zhou, Yu Li
https://arxiv.org/abs/2506.06335

FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models
In natural language processing (NLP), the focus has shifted from encoder-only tiny language models like BERT to decoder-only large language models(LLMs) such as GPT-3. However, LLMs' practical application in the financial sector has revealed three limitations: (1) LLMs often perform worse than fine-tuned BERT on discriminative tasks despite costing much higher computational resources, such as market sentiment analysis in financial reports; (2) Application on generative tasks heavily relies on r…

Advancing Cross-lingual Aspect-Based Sentiment Analysis with LLMs and Constrained Decoding for Sequence-to-Sequence Models
Jakub \v{S}m\'id, Pavel P\v{r}ib\'a\v{n}, Pavel Kr\'al
https://arxiv.org/abs/2508.10366

Advancing Cross-lingual Aspect-Based Sentiment Analysis with LLMs and Constrained Decoding for Sequence-to-Sequence Models
Aspect-based sentiment analysis (ABSA) has made significant strides, yet challenges remain for low-resource languages due to the predominant focus on English. Current cross-lingual ABSA studies often centre on simpler tasks and rely heavily on external translation tools. In this paper, we present a novel sequence-to-sequence method for compound ABSA tasks that eliminates the need for such tools. Our approach, which uses constrained decoding, improves cross-lingual ABSA performance by up to 10\%…

Improving Generative Cross-lingual Aspect-Based Sentiment Analysis with Constrained Decoding
Jakub \v{S}m\'id, Pavel P\v{r}ib\'a\v{n}, Pavel Kr\'al
https://arxiv.org/abs/2508.10369

Improving Generative Cross-lingual Aspect-Based Sentiment Analysis with Constrained Decoding
While aspect-based sentiment analysis (ABSA) has made substantial progress, challenges remain for low-resource languages, which are often overlooked in favour of English. Current cross-lingual ABSA approaches focus on limited, less complex tasks and often rely on external translation tools. This paper introduces a novel approach using constrained decoding with sequence-to-sequence models, eliminating the need for unreliable translation tools and improving cross-lingual performance by 5\% on ave…

Replaced article(s) found for q-fin.ST. https://arxiv.org/list/q-fin.ST/new/
[1/1]:
Financial sentiment analysis using FinBERT with application in predicting stock movement

On the Language and Gender Biases in PSTN, VoIP and Neural Audio Codecs
Kemal Altwlkany, Amar Kuric, Emanuel Lacic
https://arxiv.org/abs/2506.02545 https:/…

On the Language and Gender Biases in PSTN, VoIP and Neural Audio Codecs
In recent years, there has been a growing focus on fairness and inclusivity within speech technology, particularly in areas such as automatic speech recognition and speech sentiment analysis. When audio is transcoded prior to processing, as is the case in streaming or real-time applications, any inherent bias in the coding mechanism may result in disparities. This not only affects user experience but can also have broader societal implications by perpetuating stereotypes and exclusion. Thus, it…

AI in Mental Health: Emotional and Sentiment Analysis of Large Language Models' Responses to Depression, Anxiety, and Stress Queries
Arya VarastehNezhad, Reza Tavasoli, Soroush Elyasi, MohammadHossein LotfiNia, Hamed Farbeh
https://arxiv.org/abs/2508.11285

AI in Mental Health: Emotional and Sentiment Analysis of Large Language Models' Responses to Depression, Anxiety, and Stress Queries
Depression, anxiety, and stress are widespread mental health concerns that increasingly drive individuals to seek information from Large Language Models (LLMs). This study investigates how eight LLMs (Claude Sonnet, Copilot, Gemini Pro, GPT-4o, GPT-4o mini, Llama, Mixtral, and Perplexity) reply to twenty pragmatic questions about depression, anxiety, and stress when those questions are framed for six user profiles (baseline, woman, man, young, old, and university student). The models generated …

Federal Reserve Communication and the COVID-19 Pandemic
Jonathan Benchimol, Sophia Kazinnik, Yossi Saadon
https://arxiv.org/abs/2508.04830 https://arxiv.or…

Federal Reserve Communication and the COVID-19 Pandemic
In this study, we examine the Federal Reserve's communication strategies during the COVID-19 pandemic, comparing them with communication during previous periods of economic stress. Using specialized dictionaries tailored to COVID-19, unconventional monetary policy (UMP), and financial stability, combined with sentiment analysis and topic modeling techniques, we identify a distinct focus in Fed communication during the pandemic on financial stability, market volatility, social welfare, and UMP, …

Replaced article(s) found for stat.AP. https://arxiv.org/list/stat.AP/new
[1/1]:
- Comparative Firm-Level Analysis of ESG and Sentiment Effects on Stock Market Performance: Evidenc...
Sangdeok Lee

VaxPulse: Monitoring of Online Public Concerns to Enhance Post-licensure Vaccine Surveillance
Muhammad Javed, Sedigh Khademi, Joanne Hickman, Jim Buttery, Hazel Clothier, Gerardo Luis Dimaguila
https://arxiv.org/abs/2507.04656

VaxPulse: Monitoring of Online Public Concerns to Enhance Post-licensure Vaccine Surveillance
The recent vaccine-related infodemic has amplified public concerns, highlighting the need for proactive misinformation management. We describe how we enhanced the reporting surveillance system of Victoria's vaccine safety service, SAEFVIC, through the incorporation of new information sources for public sentiment analysis, topics of discussion, and hesitancies about vaccinations online. Using VaxPulse, a multi-step framework, we integrate adverse events following immunisation (AEFI) with sentime…

Analysis of LLM Bias (Chinese Propaganda & Anti-US Sentiment) in DeepSeek-R1 vs. ChatGPT o3-mini-high
PeiHsuan Huang, ZihWei Lin, Simon Imbot, WenCheng Fu, Ethan Tu
https://arxiv.org/abs/2506.01814

Analysis of LLM Bias (Chinese Propaganda & Anti-US Sentiment) in DeepSeek-R1 vs. ChatGPT o3-mini-high
Large language models (LLMs) increasingly shape public understanding and civic decisions, yet their ideological neutrality is a growing concern. While existing research has explored various forms of LLM bias, a direct, cross-lingual comparison of models with differing geopolitical alignments-specifically a PRC-system model versus a non-PRC counterpart-has been lacking. This study addresses this gap by systematically evaluating DeepSeek-R1 (PRC-aligned) against ChatGPT o3-mini-high (non-PRC) for…

Fair Play in the Newsroom: Actor-Based Filtering Gender Discrimination in Text Corpora
Stefanie Urchs, Veronika Thurner, Matthias A{\ss}enmacher, Christian Heumann, Stephanie Thiemichen
https://arxiv.org/abs/2508.13169

Fair Play in the Newsroom: Actor-Based Filtering Gender Discrimination in Text Corpora
Large language models are increasingly shaping digital communication, yet their outputs often reflect structural gender imbalances that originate from their training data. This paper presents an extended actor-level pipeline for detecting and mitigating gender discrimination in large-scale text corpora. Building on prior work in discourse-aware fairness analysis, we introduce new actor-level metrics that capture asymmetries in sentiment, syntactic agency, and quotation styles. The pipeline supp…

OSINT or BULLSHINT? Exploring Open-Source Intelligence tweets about the Russo-Ukrainian War
Johannes Niu, Mila Stillman, Anna Kruspe
https://arxiv.org/abs/2508.03599 https://

OSINT or BULLSHINT? Exploring Open-Source Intelligence tweets about the Russo-Ukrainian War
This paper examines the role of Open Source Intelligence (OSINT) on Twitter regarding the Russo-Ukrainian war, distinguishing between genuine OSINT and deceptive misinformation efforts, termed "BULLSHINT." Utilizing a dataset spanning from January 2022 to July 2023, we analyze nearly 2 million tweets from approximately 1,040 users involved in discussing real-time military engagements, strategic analyses, and misinformation related to the conflict. Using sentiment analysis, partisanship detectio…

SafeConstellations: Steering LLM Safety to Reduce Over-Refusals Through Task-Specific Trajectory
Utsav Maskey, Sumit Yadav, Mark Dras, Usman Naseem
https://arxiv.org/abs/2508.11290

SafeConstellations: Steering LLM Safety to Reduce Over-Refusals Through Task-Specific Trajectory
LLMs increasingly exhibit over-refusal behavior, where safety mechanisms cause models to reject benign instructions that superficially resemble harmful content. This phenomena diminishes utility in production applications that repeatedly rely on common prompt templates or applications that frequently rely on LLMs for specific tasks (e.g. sentiment analysis, language translation). Through comprehensive evaluation, we demonstrate that LLMs still tend to refuse responses to harmful instructions wh…

From Individuals to Crowds: Dual-Level Public Response Prediction in Social Media
Jinghui Zhang, Kaiyang Wan, Longwei Xu, Ao Li, Zongfang Liu, Xiuying Chen
https://arxiv.org/abs/2508.00497

From Individuals to Crowds: Dual-Level Public Response Prediction in Social Media
Public response prediction is critical for understanding how individuals or groups might react to specific events, policies, or social phenomena, making it highly valuable for crisis management, policy-making, and social media analysis. However, existing works face notable limitations. First, they lack micro-level personalization, producing generic responses that ignore individual user preferences. Moreover, they overlook macro-level sentiment distribution and only deal with individual-level se…

Can Large Vision-Language Models Understand Multimodal Sarcasm?
Xinyu Wang, Yue Zhang, Liqiang Jing
https://arxiv.org/abs/2508.03654 https://arxiv.org/pdf/…

Can Large Vision-Language Models Understand Multimodal Sarcasm?
Sarcasm is a complex linguistic phenomenon that involves a disparity between literal and intended meanings, making it challenging for sentiment analysis and other emotion-sensitive tasks. While traditional sarcasm detection methods primarily focus on text, recent approaches have incorporated multimodal information. However, the application of Large Visual Language Models (LVLMs) in Multimodal Sarcasm Analysis (MSA) remains underexplored. In this paper, we evaluate LVLMs in MSA tasks, specifical…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/3]:
- From Stars to Insights: Exploration and Implementation of Unified Sentiment Analysis with Distant...
Wenchang Li, John P. Lalor, Yixing Chen, Vamsi K. Kanuri

Cropping outperforms dropout as an augmentation strategy for training self-supervised text embeddings
Rita Gonz\'alez-M\'arquez, Philipp Berens, Dmitry Kobak
https://arxiv.org/abs/2508.03453

Cropping outperforms dropout as an augmentation strategy for training self-supervised text embeddings
Text embeddings, i.e. vector representations of entire texts, play an important role in many NLP applications, such as retrieval-augmented generation, sentiment analysis, clustering, or visualizing collections of texts for data exploration. Currently, top-performing embedding models are derived from pre-trained language models via extensive supervised fine-tuning using curated text pairs. This contrasts with computer vision, where self-supervised training based on data augmentations has demonst…

Adapting Language Models to Indonesian Local Languages: An Empirical Study of Language Transferability on Zero-Shot Settings
Rifki Afina Putri
https://arxiv.org/abs/2507.01645

Adapting Language Models to Indonesian Local Languages: An Empirical Study of Language Transferability on Zero-Shot Settings
In this paper, we investigate the transferability of pre-trained language models to low-resource Indonesian local languages through the task of sentiment analysis. We evaluate both zero-shot performance and adapter-based transfer on ten local languages using models of different types: a monolingual Indonesian BERT, multilingual models such as mBERT and XLM-R, and a modular adapter-based approach called MAD-X. To better understand model behavior, we group the target languages into three categori…