Sentiment Analysis and Opinion Mining

Derniers papiers

• DanceHA: A Multi-Agent Framework for Document-Level Aspect-Based Sentiment Analysis

  Lei Wang, Min Huang, Eduard Dragut · 18 mars 2026

  Aspect-Based Sentiment Intensity Analysis (ABSIA) has garnered increasing attention, though research largely focuses on domain-specific, sentence-level settings. In contrast, document-level ABSIA--particularly in addressing complex tasks like extracting Aspect-Category-Opinion-Sentiment-Intensity (A…

• Generate Then Correct: Single Shot Global Correction for Aspect Sentiment Quad Prediction

  Shidong He, Haoyu Wang, Wenjie Luo · 17 mars 2026

  Aspect-based sentiment analysis (ABSA) extracts aspect-level sentiment signals from user-generated text, supports product analytics, experience monitoring, and public-opinion tracking, and is central to fine-grained opinion mining. A key challenge in ABSA is aspect sentiment quad prediction (ASQP), …

• Decision-Level Ordinal Modeling for Multimodal Essay Scoring with Large Language Models

  Han Zhang, Jiamin Su, Li liu · 17 mars 2026

  Automated essay scoring (AES) predicts multiple rubric-defined trait scores for each essay, where each trait follows an ordered discrete rating scale. Most LLM-based AES methods cast scoring as autoregressive token generation and obtain the final score via decoding and parsing, making the decision i…

• FedUAF: Uncertainty-Aware Fusion with Reliability-Guided Aggregation for Multimodal Federated Sentiment Analysis

  Xianxun Zhu, Zezhong Sun, Imad Rida, Erik Cambria, Junqi Su, Rui Wang, Hui Chen · 17 mars 2026

  Multimodal sentiment analysis in federated learning environments faces significant challenges due to missing modalities, heterogeneous data distributions, and unreliable client updates. Existing federated approaches often struggle to maintain robust performance under these practical conditions. In t…

• Generating Expressive and Customizable Evals for Timeseries Data Analysis Agents with AgentFuel

  Aadyaa Maddi, Prakhar Naval, Deepti Mande, Shane Duan, Muckai Girish, Vyas Sekar · 16 mars 2026

  Across many domains (e.g., IoT, observability, telecommunications, cybersecurity), there is an emerging adoption of conversational data analysis agents that enable users to "talk to your data" to extract insights. Such data analysis agents operate on timeseries data models; e.g., measurements from s…

• Artificial Intelligence for Sentiment Analysis of Persian Poetry

  Arash Zargar, Abolfazl Moshiri, Mitra Shafaei, Shabnam Rahimi-Golkhandan, Mohamad Tavakoli-Targhi, Farzad Khalvati · 13 mars 2026

  Recent advancements of the Artificial Intelligence (AI) have led to the development of large language models (LLMs) that are capable of understanding, analysing, and creating textual data. These language models open a significant opportunity in analyzing the literature and more specifically poetry. …

• BTZSC: A Benchmark for Zero-Shot Text Classification Across Cross-Encoders, Embedding Models, Rerankers and LLMs

  Ilias Aarab · 13 mars 2026

  Zero-shot text classification (ZSC) offers the promise of eliminating costly task-specific annotation by matching texts directly to human-readable label descriptions. While early approaches have predominantly relied on cross-encoder models fine-tuned for natural language inference (NLI), recent adva…

• CEI: A Benchmark for Evaluating Pragmatic Reasoning in Language Models

  Jon Chun, Hannah Sussman, Adrian Mangine, Murathan Kocaman, Kirill Sidorko, Abhigya Koirala, Andre McCloud, Gwen Eisenbeis, Wisdom Akanwe, Moustapha Gassama, Eliezer Gonzalez Chirinos, Anne-Duncan Enright, Peter Dunson, Tiffanie Ng, Anna von Rosenstiel, Godwin Idowu · 12 mars 2026

  Pragmatic reasoning, inferring intended meaning beyond literal semantics, underpins everyday communication yet remains difficult for large language models. We present the Contextual Emotional Inference (CEI) Benchmark: 300 human-validated scenarios for evaluating how well LLMs disambiguate pragmatic…

• Designing Service Systems from Textual Evidence

  Ruicheng Ao, Hongyu Chen, Siyang Gao, Hanwei Li, David Simchi-Levi · 12 mars 2026

  Designing service systems requires selecting among alternative configurations -- choosing the best chatbot variant, the optimal routing policy, or the most effective quality control procedure. In many service systems, the primary evidence of performance quality is textual -- customer support transcr…

• PoultryLeX-Net: Domain-Adaptive Dual-Stream Transformer Architecture for Large-Scale Poultry Stakeholder Modeling

  Stephen Afrifa, Biswash Khatiwada, Kapalik Khanal, Sanjay Shah, Lingjuan Wang-Li, Ramesh Bahadur Bist · 12 mars 2026

  The rapid growth of the global poultry industry, driven by rising demand for affordable animal protein, has intensified public discourse surrounding production practices, housing, management, animal welfare, and supply-chain transparency. Social media platforms such as X (formerly Twitter) generate …

• Emotion is Not Just a Label: Latent Emotional Factors in LLM Processing

  Benjamin Reichman, Adar Avasian, Samuel Webster, Larry Heck · 11 mars 2026

  Large language models are routinely deployed on text that varies widely in emotional tone, yet their reasoning behavior is typically evaluated without accounting for emotion as a source of representational variation. Prior work has largely treated emotion as a prediction target, for example in senti…

• Benchmarking Political Persuasion Risks Across Frontier Large Language Models

  Zhongren Chen, Joshua Kalla, Quan Le · 11 mars 2026

  Concerns persist regarding the capacity of Large Language Models (LLMs) to sway political views. Although prior research has claimed that LLMs are not more persuasive than standard political campaign practices, the recent rise of frontier models warrants further study. In two survey experiments (N=1…

• Sensory-Aware Sequential Recommendation via Review-Distilled Representations

  Yeo Chan Yoon · 4 mars 2026

  We propose a novel framework for sensory-aware sequential recommendation that enriches item representations with linguistically extracted sensory attributes from product reviews. Our approach, \textsc{ASEGR} (Attribute-based Sensory Enhanced Generative Recommendation), introduces a two-stage pipelin…

• Task Complexity Matters: An Empirical Study of Reasoning in LLMs for Sentiment Analysis

  Donghao Huang, Zhaoxia Wang · 2 mars 2026

  Large language models (LLMs) with reasoning capabilities have fueled a compelling narrative that reasoning universally improves performance across language tasks. We test this claim through a comprehensive evaluation of 504 configurations across seven model families--including adaptive, conditional,…

• M3TR: Temporal Retrieval Enhanced Multi-Modal Micro-video Popularity Prediction

  Jiacheng Lu, Weijian Wang, Mingyuan Xiao, Yang Hua, Tao Song, Jiaru Zhang, Bo Peng, Cheng Hua, Haibing Guan · 2 mars 2026

  Accurately predicting the popularity of micro-videos is a critical but challenging task, characterized by volatile, `rollercoaster-like' engagement dynamics. Existing methods often fail to capture these complex temporal patterns, leading to inaccurate long-term forecasts. This failure stems from two…

• Multi-Agent Large Language Model Based Emotional Detoxification Through Personalized Intensity Control for Consumer Protection

  Keito Inoshita · 27 février 2026

  In the attention economy, sensational content exposes consumers to excessive emotional stimulation, hindering calm decision-making. This study proposes Multi-Agent LLM-based Emotional deToxification (MALLET), a multi-agent information sanitization system consisting of four agents: Emotion Analysis, …

• Evaluating Cross-Lingual Classification Approaches Enabling Topic Discovery for Multilingual Social Media Data

  Deepak Uniyal, Md Abul Bashar, Richi Nayak · 20 février 2026

  Analysing multilingual social media discourse remains a major challenge in natural language processing, particularly when large-scale public debates span across diverse languages. This study investigates how different approaches for cross-lingual text classification can support reliable analysis of …

• Aspect-Based Sentiment Analysis for Future Tourism Experiences: A BERT-MoE Framework for Persian User Reviews

  Hamidreza Kazemi Taskooh, Taha Zare Harofte · 16 février 2026

  This study advances aspect-based sentiment analysis (ABSA) for Persian-language user reviews in the tourism domain, addressing challenges of low-resource languages. We propose a hybrid BERT-based model with Top-K routing and auxiliary losses to mitigate routing collapse and improve efficiency. The p…

• Structured Sentiment Analysis as Transition-based Dependency Graph Parsing

  Daniel Fern\'andez-Gonz\'alez · 12 février 2026

  Structured sentiment analysis (SSA) aims to automatically extract people's opinions from a text in natural language and adequately represent that information in a graph structure. One of the most accurate methods for performing SSA was recently proposed and consists of approaching it as a dependency…

• MS-Mix: Unveiling the Power of Mixup for Multimodal Sentiment Analysis

  Hongyu Zhu, Lin Chen, Xin Jin, Mounim A. El-Yacoubi, Mingsheng Shang · 10 février 2026

  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 annot…

• Don't Always Pick the Highest-Performing Model: An Information Theoretic View of LLM Ensemble Selection

  Yigit Turkmen, Baturalp Buyukates, Melih Bastopcu · 10 février 2026

  Large language models (LLMs) are often ensembled together to improve overall reliability and robustness, but in practice models are strongly correlated. This raises a fundamental question: which models should be selected when forming an LLM ensemble? We formulate budgeted ensemble selection as maxim…

• Don't Always Pick the Highest-Performing Model: An Information Theoretic View of LLM Ensemble Selection

  Yigit Turkmen, Baturalp Buyukates, Melih Bastopcu · 10 février 2026

  Large language models (LLMs) are often ensembled together to improve overall reliability and robustness, but in practice models are strongly correlated. This raises a fundamental question: which models should be selected when forming an LLM ensemble? We formulate budgeted ensemble selection as maxim…

• A Statistical Framework for Alignment with Biased AI Feedback

  Xintao Xia, Zhiqiu Xia, Linjun Zhang, Zhanrui Cai · 10 février 2026

  Modern alignment pipelines are increasingly replacing expensive human preference labels with evaluations from large language models (LLM-as-Judge). However, AI labels can be systematically biased compared to high-quality human feedback datasets. In this paper, we develop two debiased alignment metho…

• Investigating the structure of emotions by analyzing similarity and association of emotion words

  Fumitaka Iwaki, Tatsuji Takahashi · 9 février 2026

  In the field of natural language processing, some studies have attempted sentiment analysis on text by handling emotions as explanatory or response variables. One of the most popular emotion models used in this context is the wheel of emotion proposed by Plutchik. This model schematizes human emotio…

• STAR: Stepwise Task Augmentation with Relation Learning for Aspect Sentiment Quad Prediction

  Wenna Lai, Haoran Xie, Guandong Xu, Qing Li · 9 février 2026

  Aspect-based sentiment analysis (ABSA) aims to identify four sentiment elements, including aspect term, aspect category, opinion term, and sentiment polarity. These elements construct a complete picture of sentiments. The most challenging task, aspect sentiment quad prediction (ASQP), requires predi…