Sentiment Analysis and Opinion Mining

Últimos artículos

• Exploiting contextual information to improve stance detection in informal political discourse with LLMs

  Arman Engin Sucu, Yixiang Zhou, Mario A. Nascimento, Tony Mullen · 5 de febrero de 2026

  This study investigates the use of Large Language Models (LLMs) for political stance detection in informal online discourse, where language is often sarcastic, ambiguous, and context-dependent. We explore whether providing contextual information, specifically user profile summaries derived from hist…

• SentiFuse: Deep Multi-model Fusion Framework for Robust Sentiment Extraction

  Hieu Minh Duong, Rupa Ghosh, Cong Hoan Nguyen, Eugene Levin, Todd Gary, Long Nguyen · 3 de febrero de 2026

  Sentiment analysis models exhibit complementary strengths, yet existing approaches lack a unified framework for effective integration. We present SentiFuse, a flexible and model-agnostic framework that integrates heterogeneous sentiment models through a standardization layer and multiple fusion stra…

• Leveraging Textual-Cues for Enhancing Multimodal Sentiment Analysis by Object Recognition

  Sumana Biswas, Karen Young, Josephine Griffith · 3 de febrero de 2026

  Multimodal sentiment analysis, which includes both image and text data, presents several challenges due to the dissimilarities in the modalities of text and image, the ambiguity of sentiment, and the complexities of contextual meaning. In this work, we experiment with finding the sentiments of image…

• Large Language Model and Formal Concept Analysis: a comparative study for Topic Modeling

  Fabrice Boissier (CRI), Monica Sen (UP1 UFR27), Irina Rychkova (CRI) · 3 de febrero de 2026

  Topic modeling is a research field finding increasing applications: historically from document retrieving, to sentiment analysis and text summarization. Large Language Models (LLM) are currently a major trend in text processing, but few works study their usefulness for this task. Formal Concept Anal…

• Invariant Representation Guided Multimodal Sentiment Decoding with Sequential Variation Regularization

  Guoyang Xu, Zhenxi Song, Junqi Xue, Yuxin Liu, Zirui Wang, Zhiguo Zhang · 3 de febrero de 2026

  Achieving consistent sentiment representation across diverse modalities remains a key challenge in multimodal sentiment analysis. However, rapid emotional fluctuations over time often introduce instability, leading to compromised prediction performance. To address this challenge, we propose a robust…

• Topeax -- An Improved Clustering Topic Model with Density Peak Detection and Lexical-Semantic Term Importance

  M\'arton Kardos · 30 de enero de 2026

  Text clustering is today the most popular paradigm for topic modelling, both in academia and industry. Despite clustering topic models' apparent success, we identify a number of issues in Top2Vec and BERTopic, which remain largely unsolved. Firstly, these approaches are unreliable at discovering nat…

• GDCNet: Generative Discrepancy Comparison Network for Multimodal Sarcasm Detection

  Shuguang Zhang, Junhong Lian, Guoxin Yu, Baoxun Xu, Xiang Ao · 29 de enero de 2026

  Multimodal sarcasm detection (MSD) aims to identify sarcasm within image-text pairs by modeling semantic incongruities across modalities. Existing methods often exploit cross-modal embedding misalignment to detect inconsistency but struggle when visual and textual content are loosely related or sema…

• A Hybrid Supervised-LLM Pipeline for Actionable Suggestion Mining in Unstructured Customer Reviews

  Aakash Trivedi, Aniket Upadhyay, Pratik Narang, Dhruv Kumar, Praveen Kumar · 28 de enero de 2026

  Extracting actionable suggestions from customer reviews is essential for operational decision-making, yet these directives are often embedded within mixed-intent, unstructured text. Existing approaches either classify suggestion-bearing sentences or generate high-level summaries, but rarely isolate …

• UrduLM: A Resource-Efficient Monolingual Urdu Language Model

  Syed Muhammad Ali, Hammad Sajid, Zainab Haider, Ali Muhammad Asad, Haya Fatima, Abdul Samad · 27 de enero de 2026

  Urdu, spoken by 230 million people worldwide, lacks dedicated transformer-based language models and curated corpora. While multilingual models provide limited Urdu support, they suffer from poor performance, high computational costs, and cultural inaccuracies due to insufficient training data. To ad…

• Funny or Persuasive, but Not Both: Evaluating Fine-Grained Multi-Concept Control in LLMs

  Arya Labroo, Ivaxi Sheth, Vyas Raina, Amaani Ahmed, Mario Fritz · 27 de enero de 2026

  Large Language Models (LLMs) offer strong generative capabilities, but many applications require explicit and \textit{fine-grained} control over specific textual concepts, such as humor, persuasiveness, or formality. Prior approaches in prompting and representation engineering can provide coarse or …

• ANUBHUTI: A Comprehensive Corpus For Sentiment Analysis In Bangla Regional Languages

  Swastika Kundu, Autoshi Ibrahim, Mithila Rahman, Tanvir Ahmed · 21 de enero de 2026

  Sentiment analysis for regional dialects of Bangla remains an underexplored area due to linguistic diversity and limited annotated data. This paper introduces ANUBHUTI, a comprehensive dataset consisting of 10,000 sentences manually translated from standard Bangla into four major regional dialects M…

• Actionable Advice from Reviews via Mixture of LoRA Experts: A Two-LLM Pipeline for Issue Extraction and Business Recommendations

  Kartikey Singh Bhandari, Manav Ganesh, Yashwant Viswanathan, Archit Agrawal, Dhruv Kumar, Pratik Narang · 21 de enero de 2026

  Customer reviews contain detailed, domain specific signals about service failures and user expectations, but converting this unstructured feedback into actionable business decisions remains difficult. We study review-to-action generation: producing concrete, implementable recommendations grounded in…

• A Multi-Agent System for Generating Actionable Business Advice

  Kartikey Singh Bhandari, Tanish Jain, Archit Agrawal, Dhruv Kumar, Praveen Kumar, Pratik Narang · 21 de enero de 2026

  Customer reviews contain rich signals about product weaknesses and unmet user needs, yet existing analytic methods rarely move beyond descriptive tasks such as sentiment analysis or aspect extraction. While large language models (LLMs) can generate free-form suggestions, their outputs often lack acc…

• MoLAN: A Unified Modality-Aware Noise Dynamic Editing Framework for Multimodal Sentiment Analysis

  Xingle Xu, Yongkang Liu, Dexian Cai, Shi Feng, Xiaocui Yang, Daling Wang, Yifei Zhang · 19 de enero de 2026

  Multimodal Sentiment Analysis aims to integrate information from various modalities, such as audio, visual, and text, to make complementary predictions. However, it often struggles with irrelevant or misleading visual and auditory information. Most existing approaches typically treat the entire moda…

• KPoEM: A Human-Annotated Dataset for Emotion Classification and RAG-Based Poetry Generation in Korean Modern Poetry

  Iro Lim, Haein Ji, Byungjun Kim · 15 de enero de 2026

  This study introduces KPoEM (Korean Poetry Emotion Mapping), a novel dataset that serves as a foundation for both emotion-centered analysis and generative applications in modern Korean poetry. Despite advancements in NLP, poetry remains underexplored due to its complex figurative language and cultur…

• Enhancing Sentiment Classification and Irony Detection in Large Language Models through Advanced Prompt Engineering Techniques

  Marvin Schmitt, Anne Schwerk, Sebastian Lempert · 14 de enero de 2026

  This study investigates the use of prompt engineering to enhance large language models (LLMs), specifically GPT-4o-mini and gemini-1.5-flash, in sentiment analysis tasks. It evaluates advanced prompting techniques like few-shot learning, chain-of-thought prompting, and self-consistency against a bas…

• Multi-Stage Evolutionary Model Merging with Meta Data Driven Curriculum Learning for Sentiment-Specialized Large Language Modeling

  Keito Inoshita, Xiaokang Zhou, Akira Kawai · 13 de enero de 2026

  The emergence of large language models (LLMs) has significantly transformed natural language processing (NLP), enabling more generalized models to perform various tasks with minimal training. However, traditional sentiment analysis methods, which focus on individual tasks such as sentiment classific…

• SemCSE-Multi: Multifaceted and Decodable Embeddings for Aspect-Specific and Interpretable Scientific Domain Mapping

  Marc Brinner, Sina Zarrie{\ss} · 13 de enero de 2026

  We propose SemCSE-Multi, a novel unsupervised framework for generating multifaceted embeddings of scientific abstracts, evaluated in the domains of invasion biology and medicine. These embeddings capture distinct, individually specifiable aspects in isolation, thus enabling fine-grained and controll…

• IndRegBias: A Dataset for Studying Indian Regional Biases in English and Code-Mixed Social Media Comments

  Debasmita Panda, Akash Anil, Neelesh Kumar Shukla · 13 de enero de 2026

  Warning: This paper consists of examples representing regional biases in Indian regions that might be offensive towards a particular region. While social biases corresponding to gender, race, socio-economic conditions, etc., have been extensively studied in the major applications of Natural Language…

• A Multi-Stage Workflow for the Review of Marketing Content with Reasoning Large Language Models

  Alberto Purpura, Emily Chen, Swapnil Shinde · 13 de enero de 2026

  Reasoning Large Language Models (LLMs) have shown promising results when tasked with solving complex problems. In this paper, we propose and evaluate a multi-stage workflow that leverages the capabilities of fine-tuned reasoning LLMs to assist in the review process of marketing content, making sure …

• Transforming User Defined Criteria into Explainable Indicators with an Integrated LLM AHP System

  Geonwoo Bang, Dongho Kim, Moohong Min · 12 de enero de 2026

  Evaluating complex texts across domains requires converting user defined criteria into quantitative, explainable indicators, which is a persistent challenge in search and recommendation systems. Single prompt LLM evaluations suffer from complexity and latency issues, while criterion specific decompo…

• Automatic Classifiers Underdetect Emotions Expressed by Men

  Ivan Smirnov, Segun T. Aroyehun, Paul Plener, David Garcia · 9 de enero de 2026

  The widespread adoption of automatic sentiment and emotion classifiers makes it important to ensure that these tools perform reliably across different populations. Yet their reliability is typically assessed using benchmarks that rely on third-party annotators rather than the individuals experiencin…

• Code-Mix Sentiment Analysis on Hinglish Tweets

  Aashi Garg, Aneshya Das, Arshi Arya, Anushka Goyal, Aditi · 9 de enero de 2026

  The effectiveness of brand monitoring in India is increasingly challenged by the rise of Hinglish--a hybrid of Hindi and English--used widely in user-generated content on platforms like Twitter. Traditional Natural Language Processing (NLP) models, built for monolingual data, often fail to interpret…

• Ideology as a Problem: Lightweight Logit Steering for Annotator-Specific Alignment in Social Media Analysis

  Wei Xia, Haowen Tang, Luozheng Li · 9 de enero de 2026

  LLMs internally organize political ideology along low-dimensional structures that are partially, but not fully aligned with human ideological space. This misalignment is systematic, model specific, and measurable. We introduce a lightweight linear probe that both quantifies the misalignment and mini…

• Grad-ELLM: Gradient-based Explanations for Decoder-only LLMs

  Xin Huang, Antoni B. Chan · 7 de enero de 2026

  Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, yet their black-box nature raises concerns about transparency and faithfulness. Input attribution methods aim to highlight each input token's contributions to the model's output, but existing approaches are …