Sentiment Analysis and Opinion Mining

Neueste Paper

• $\mathcal{S}^2$IT: Stepwise Syntax Integration Tuning for Large Language Models in Aspect Sentiment Quad Prediction

  Bingfeng Chen, Chenjie Qiu, Yifeng Xie, Boyan Xu, Ruichu Cai, Zhifeng Hao · 29. April 2026

  Aspect Sentiment Quad Prediction (ASQP) has seen significant advancements, largely driven by the powerful semantic understanding and generative capabilities of large language models (LLMs). However, while syntactic structure information has been proven effective in previous extractive paradigms, it …

• Two-dimensional early exit optimisation of LLM inference

  Jan H\r{u}la, David Adamczyk, Tom\'a\v{s} Filip, Martin Pavl\'i\v{c}ek, Petr Sos\'ik · 22. April 2026

  We introduce a two-dimensional (2D) early exit strategy that coordinates layer-wise and sentence-wise exiting for classification tasks in large language models. By processing input incrementally sentence-by-sentence while progressively activating deeper layers, our method achieves multiplicative com…

• EduRABSA: An Education Review Dataset for Aspect-based Sentiment Analysis Tasks

  Yan Cathy Hua, Paul Denny, J\"org Wicker, Katerina Taskova · 21. April 2026

  Every year, most educational institutions seek and receive an enormous volume of text feedback from students on courses, teaching, and overall experience. Yet, turning this raw feedback into useful insights is far from straightforward. It has been a long-standing challenge to adopt automatic opinion…

• Consistency Analysis of Sentiment Predictions using Syntactic & Semantic Context Assessment Summarization (SSAS)

  Sharookh Daruwalla, Nitin Mayande, Shreeya Verma Kathuria, Nitin Joglekar, Charles Weber · 20. April 2026

  The fundamental challenge of using Large Language Models (LLMs) for reliable, enterprise-grade analytics, such as sentiment prediction, is the conflict between the LLMs' inherent stochasticity (generative, non-deterministic nature) and the analytical requirement for consistency. The LLM inconsistenc…

• When Missing Becomes Structure: Intent-Preserving Policy Completion from Financial KOL Discourse

  Yuncong Liu, Yuan Wan, Zhou Jiang, Yao Lu · 17. April 2026

  Key Opinion Leader (KOL) discourse on social media is widely consumed as investment guidance, yet turning it into executable trading strategies without injecting assumptions about unspecified execution decisions remains an open problem. We observe that the gaps in KOL statements are not random defic…

• Decoupling Scores and Text: The Politeness Principle in Peer Review

  Yingxuan Wen · 17. April 2026

  Authors often struggle to interpret peer review feedback, deriving false hope from polite comments or feeling confused by specific low scores. To investigate this, we construct a dataset of over 30,000 ICLR 2021-2025 submissions and compare acceptance prediction performance using numerical scores ve…

• Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go?

  Reem Alfayez, Manal Binkhonain · 16. April 2026

  Sentiment analysis in software engineering focuses on understanding emotions expressed in software artifacts. Previous research highlighted the limitations of applying general off-the-shelf sentiment analysis tools within the software engineering domain and indicated the need for specialized tools t…

• From Prediction to Justification: Aligning Sentiment Reasoning with Human Rationale via Reinforcement Learning

  Shihao Zhang, Ziwei Wang, Jie Zhou, Yulan Wu, Qin Chen, Zhikai Lei, Liyang Yu, Liang Dou, Liang He · 16. April 2026

  While Aspect-based Sentiment Analysis (ABSA) systems have achieved high accuracy in identifying sentiment polarities, they often operate as "black boxes," lacking the explicit reasoning capabilities characteristic of human affective cognition. Humans do not merely categorize sentiment; they construc…

• Beyond Factual Grounding: The Case for Opinion-Aware Retrieval-Augmented Generation

  Aditya Agrawal, Alwarappan Nakkiran, Darshan Fofadiya, Alex Karlsson, Harsha Aduri · 15. April 2026

  RAG systems have transformed how LLMs access external knowledge, but we find that current implementations exhibit a bias toward factual, objective content, as evidenced by existing benchmarks and datasets that prioritize objective retrieval. This factual bias - treating opinions and diverse perspect…

• C2F-Thinker: Coarse-to-Fine Reasoning with Hint-Guided Reinforcement Learning for Multimodal Sentiment Analysis

  Miaosen Luo, Zhenhao Yang, Jieshen Long, Jinghu Sun, Yichu Liu, Sijie Mai · 14. April 2026

  Multimodal sentiment analysis aims to integrate textual, acoustic, and visual information for deep emotional understanding. Despite the progress of multimodal large language models (MLLMs) via supervised fine-tuning, their "black-box" nature hinders interpretability. While Chain-of-Thought (CoT) rea…

• GLEaN: A Text-to-image Bias Detection Approach for Public Comprehension

  Bochu Ding, Brinnae Bent, Augustus Wendell · 14. April 2026

  Text-to-image (T2I) models, and their encoded biases, increasingly shape the visual media the public encounters. While researchers have produced a rich body of work on bias measurement, auditing, and mitigation in T2I systems, those methods largely target technical stakeholders, leaving a gap in pub…

• LOLGORITHM: Funny Comment Generation Agent For Short Videos

  Xuan Ouyang, Senan Wang, Bouzhou Wang, Siyuan Xiahou, Jinrong Zhou, Yuekang Li · 14. April 2026

  Short-form video platforms have become central to multimedia information dissemination, where comments play a critical role in driving engagement, propagation, and algorithmic feedback. However, existing approaches -- including video summarization and live-streaming danmaku generation -- fail to pro…

• URMF: Uncertainty-aware Robust Multimodal Fusion for Multimodal Sarcasm Detection

  Zhenyu Wang, Weichen Cheng, Weijia Li, Junjie Mou, Zongyou Zhao, Guoying Zhang · 10. April 2026

  Multimodal sarcasm detection (MSD) aims to identify sarcastic intent from semantic incongruity between text and image. Although recent methods have improved MSD through cross-modal interaction and incongruity reasoning, they often assume that all modalities are equally reliable. In real-world social…

• Environmental, Social and Governance Sentiment Analysis on Slovene News: A Novel Dataset and Models

  Paula Dodig, Boshko Koloski, Katarina Sitar \v{S}u\v{s}tar, Senja Pollak, Matthew Purver · 10. April 2026

  Environmental, Social, and Governance (ESG) considerations are increasingly integral to assessing corporate performance, reputation, and long-term sustainability. Yet, reliable ESG ratings remain limited for smaller companies and emerging markets. We introduce the first publicly available Slovene ES…

• Digital Skin, Digital Bias: Uncovering Tone-Based Biases in LLMs and Emoji Embeddings

  Mingchen Li, Wajdi Aljedaani, Yingjie Liu, Navyasri Meka, Xuan Lu, Xinyue Ye, Junhua Ding, Yunhe Feng · 10. April 2026

  Skin-toned emojis are crucial for fostering personal identity and social inclusion in online communication. As AI models, particularly Large Language Models (LLMs), increasingly mediate interactions on web platforms, the risk that these systems perpetuate societal biases through their representation…

• Evaluating LLMs for Demographic-Targeted Social Bias Detection: A Comprehensive Benchmark Study

  Ayan Majumdar, Feihao Chen, Jinghui Li, Xiaozhen Wang · 10. April 2026

  Large-scale web-scraped text corpora used to train general-purpose AI models often contain harmful demographic-targeted social biases, creating a regulatory need for data auditing and developing scalable bias-detection methods. Although prior work has investigated biases in text datasets and related…

• QA-MoE: Towards a Continuous Reliability Spectrum with Quality-Aware Mixture of Experts for Robust Multimodal Sentiment Analysis

  Yitong Zhu, Yuxuan Jiang, Guanxuan Jiang, Bojing Hou, Peng Yuan Zhou, Ge Lin Kan, Yuyang Wang · 8. April 2026

  Multimodal Sentiment Analysis (MSA) aims to infer human sentiment from textual, acoustic, and visual signals. In real-world scenarios, however, multimodal inputs are often compromised by dynamic noise or modality missingness. Existing methods typically treat these imperfections as discrete cases or …

• Extracting and Steering Emotion Representations in Small Language Models: A Methodological Comparison

  Jihoon Jeong · 7. April 2026

  Small language models (SLMs) in the 100M-10B parameter range increasingly power production systems, yet whether they possess the internal emotion representations recently discovered in frontier models remains unknown. We present the first comparative analysis of emotion vector extraction methods for…

• MS-Mix: Sentiment-Guided Adaptive Augmentation for Multimodal Sentiment Analysis

  Hongyu Zhu, Lin Chen, Xin Jin, Mingsheng Shang · 3. April 2026

  Multimodal Sentiment Analysis (MSA) integrates complementary features from text, video, and audio for robust emotion understanding in human interactions. However, models suffer from severe data scarcity and high annotation costs, severely limiting real-world deployment in social media analytics and …

• MSA-Thinker: Discrimination-Calibration Reasoning with Hint-Guided Reinforcement Learning for Multimodal Sentiment Analysis

  Miaosen Luo, Zhenhao Yang, Jieshen Long, Jinghu Sun, Yichu Liu, Sijie Mai · 2. April 2026

  Multimodal sentiment analysis aims to understand human emotions by integrating textual, auditory, and visual modalities. Although Multimodal Large Language Models (MLLMs) have achieved state-of-the-art performance via supervised fine-tuning (SFT), their end-to-end "black-box" nature limits interpret…

• Decoding Market Emotions in Cryptocurrency Tweets via Predictive Statement Classification with Machine Learning and Transformers

  Moein Shahiki Tash, Zahra Ahani, Mohim Tash, Mostafa Keikhay Farzaneh, Ari Y. Barrera-Animas, Olga Kolesnikova · 27. März 2026

  The growing prominence of cryptocurrencies has triggered widespread public engagement and increased speculative activity, particularly on social media platforms. This study introduces a novel classification framework for identifying predictive statements in cryptocurrency-related tweets, focusing on…

• LogSigma at SemEval-2026 Task 3: Uncertainty-Weighted Multitask Learning for Dimensional Aspect-Based Sentiment Analysis

  Baraa Hikal, Jonas Becker, Bela Gipp · 27. März 2026

  This paper describes LogSigma, our system for SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA). Unlike traditional Aspect-Based Sentiment Analysis (ABSA), which predicts discrete sentiment labels, DimABSA requires predicting continuous Valence and Arousal (VA) scores on a 1…

• NDT: Non-Differential Transformer and Its Application to Sentiment Analysis

  Soudeep Ghoshal, Himanshu Buckchash, Sarita Paudel, Rub\'en Ruiz-Torrubiano · 24. März 2026

  From customer feedback to social media, understanding human sentiment in text is central to how machines can interact meaningfully with people. However, despite notable progress, accurately capturing sentiment remains a challenging task, which continues to motivate further research in this area. To …

• Enhancing Hyperspace Analogue to Language (HAL) Representations via Attention-Based Pooling for Text Classification

  Ali Sakour, Zoalfekar Sakour · 23. März 2026

  The Hyperspace Analogue to Language (HAL) model relies on global word co-occurrence matrices to construct distributional semantic representations. While these representations capture lexical relationships effectively, aggregating them into sentence-level embeddings via standard mean pooling often re…

• MOSAIC: Modular Opinion Summarization using Aspect Identification and Clustering

  Piyush Kumar Singh, Jayesh Choudhari · 23. März 2026

  Reviews are central to how travelers evaluate products on online marketplaces, yet existing summarization research often emphasizes end-to-end quality while overlooking benchmark reliability and the practical utility of granular insights. To address this, we propose MOSAIC, a scalable, modular frame…