Sentiment Analysis and Opinion Mining

Derniers papiers

• CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis

  Jianfei Li, Kevin Kam Fung Yuen · 7 août 2026

  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, Speci…

• Beyond Sentiment: Comparing Traditional NLP and LLM-Based Multi-Dimensional Analysis for Political News Evaluation

  Maryam Fooladi, Federico Bottino · 7 août 2026

  Traditional sentiment analysis (SA) models, while effective for polarity classification, provide limited insight into the rhetorical, ideological, and framing dimensions of political discourse -- dimensions that are central to research in the social sciences and humanities (SSH). In this paper, we p…

• TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis

  Isabel Xu (The Overlake School), Cynthia Xu (The Overlake School), Rachel Ren (Edwards Vacuum Inc.), Cong Guo (The University of Memphis), Jiacheng Ding (The University of Memphis) · 23 juillet 2026

  Production LLM-based financial sentiment analysis faces a structural cost trap: most queries are trivially classifiable, yet expensive cloud reasoners process them all, and the bill scales linearly with user count. We present TriAgent, a multi-agent committee stratified by contextual granularity -- …

• Semantic Primes as Explanans for Emotion in Large Language Models

  Frank Xing · 22 juillet 2026

  Progresses have been made on understanding emotion mechanisms of large language models (LLMs). However, how to explain emotion in LLMs, or even what constitutes good explanations, are less clear. Emotion representations, components, circuits are widely recoverable, but as explanations of a model's o…

• How Much of a 10-K Matters? Aggregation-Dependent Value of Full-Text versus Risk-Factor Sentiment

  Sanggyu Sean Choi · 17 juillet 2026

  Financial sentiment extraction has largely relied on news text and supervised extraction against return labels alone, leaving 10-K filings -- and volatility, the target risk disclosure is arguably best suited to informing -- comparatively unexplored. We extend a supervised lexicon-learning approach …

• Large Language Models for Token-Efficient and Semantic-Preserving Opinion Summarization

  Fabrizio Marozzo, Stefano Iannicelli · 14 juillet 2026

  Opinionated text - spanning product reviews, hotel feedback, and social posts - captures rich signals about user experiences, preferences, and concerns. However, the scale, redundancy, and imbalance of such corpora make it challenging to analyze opinions effectively, particularly when the goal is to…

• A Corpus of Persuasion Techniques in Slavic Languages

  Jakub Piskorski, Dimitar Iliyanov Dimitrov, Marina Ernst, Jacek Haneczok, Micha{\l} Marci\'nczuk, Arkadiusz Modzelewski, Roman Yangarber · 14 juillet 2026

  Persuasion techniques are powerful rhetorical devices used to sway public opinion in a wide range of media. We present a new corpus of persuasion techniques, focusing on Slavic languages. The corpus contains documents in Bulgarian, Polish, and Russian, annotated with persuasion techniques at the coa…

• A Durability and Cross-Language Transfer Benchmark for a Validated Teaching-Feedback Classification Protocol

  Esteban U. Vega Barajas · 14 juillet 2026

  Institutions collect far more open-ended teaching-evaluation feedback than they read. A prior study introduced a validated protocol for classifying such comments by thematic category and sentiment, built from a documented annotation guide, an intra-annotator reliability measurement, stratified cross…

• Conceptual Networks for Cross-Linguistic Idiomatic Expressions:A Feature-Based Graph Approach

  Kiran Pala, Punam Silu, Lixun Yu · 13 juillet 2026

  We present an interpretable network-based framework for representing idiomatic and figurative meaning across eight typologically diverse languages, totaling 160 conventional expressions, the large majority of which are idiomatic. Each expression is annotated with binary conceptual features (containm…

• Automatic Thematic Indexing of Large Literary Corpora: A Machine Learning Approach to Voltaire's Complete Works

  Miguel Arana-Catania, Gillian Pink, Glenn Roe · 13 juillet 2026

  Thematic indexing -- the practice of assigning structured conceptual labels to sections of text -- is essential to scholarly access in large-scale literary and historical editions, yet it remains a largely manual, labour-intensive process. This paper explores the application of machine learning to a…

• PLURAL: A Global Dataset for Value Alignment

  Dhruv Agarwal, Anya Shukla, Tanya Goyal, Aditya Vashistha · 10 juillet 2026

  Large language models (LLMs) are used worldwide, yet disproportionately reflect Western values, limiting their ability to represent diverse value systems. We introduce PLURAL, a large-scale, value-focused preference dataset grounded in the Integrated Values Survey (IVS), a nationally representative …

• Audio Sentiment Analysis via Distillation and Cross-Modal Integration of Generated Multilingual Transcripts

  Andrei-George Durdun, Victor Constantinescu, Radu Tudor Ionescu · 9 juillet 2026

  Automatically recognizing the sentiment, positive or negative, from speech is a challenging task, requiring both the analysis of vocal inflections and the interpretation of uttered words. Recent solutions rely on audio foundation models to solve the task, but it remains unclear if such models can ta…

• SynthAVE: Scalable Synthetic Labeling for E-Commerce with LLM-Arena Validation

  Andrea Scarinci, Virginia Negri, Brayan Impata, Suleiman Khan, Victor Martinez, Marcello Federico · 9 juillet 2026

  Fine-tuning large language models (LLMs) for e-commerce attribute extraction requires labeled data representative across thousands of product types, attributes, and multiple languages. This combinatorial scale translates to millions of annotations, rendering human labeling prohibitively costly. Whil…

• Is Domain Adaptation Always Helpful? A Frozen-Backbone Study of Cross-Domain Sentiment Transfer

  Phat Tran, Artin Lahni, Pranav Kulkarni, Yaolun Zhang · 8 juillet 2026

  Sentiment analysis with frozen pre-trained language model (PLM) backbones has become a common paradigm, yet the practical benefit of explicit domain adaptation remains unclear, particularly when backbones encode varying degrees of target-domain knowledge. We present a preliminary case study evaluati…

• SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis

  Lakshani Galwatta, Nisansa de Silva, Sarangi Aththanayake, Adithya Galwatta · 7 juillet 2026

  Sentiment analysis has been a primary domain under Natural Language Processing (NLP) from its inception as it plays a vital role in both real-world and research applications. In high-resource languages, this has been extended a step further, and instead of predicting sentiment at the sentence level,…

• Verifiable Knowledge Expansion through Retrieval-Grounded Formal Concept Analysis

  Yujin Yang, Heejung Lee · 3 juillet 2026

  Ontology construction requires deciding which objects, attributes, and structural relations should be accepted as valid knowledge. Language models can propose such structures from text, but their outputs can still be unsupported or inconsistent. This paper proposes a retrieval-augmented small langua…

• Faithful by Definition: Emotion Analysis via Natural Semantic Metalanguage Explications

  Frank Xing, Erik Cambria · 2 juillet 2026

  Explanations for emotion classifiers are usually produced post hoc, with no guarantee that they reflect the computation behind the label. We present an explication interface for event-based emotion analysis. A parser maps the input text to an explication, a short script in the closed vocabulary of N…

• A Comparative Study on Affective Cues in Text Embeddings Across Psychological Emotion Theories

  Fabio Ciani, Harald Schweiger, Emilia Parada-Cabaleiro, Markus Schedl · 30 juin 2026

  Text encoders are known for their utility in natural language processing, as they are able to efficiently compress inputs into dense vectors while preserving semantics. These models have been applied to affective computing, in particular to help with solving sentiment analysis and emotion recognitio…

• Benchmarking Multi-Modal Graph-based Social Media Popularity Prediction

  Utkarsh Sahu, Zhisheng Qi, Li Zhu, Yizhao Yang, Jun Li, Ryan Rossi, Yu Wang · 29 juin 2026

  Social media popularity prediction aims to forecast the future reach or influence of online content from early-stage observations. Accurate prediction enables key downstream applications, such as advertising optimization and strategic content planning by users, creators, and platforms. Despite subst…

• LLM-based Models for Detecting Emerging Topics in Service Feedback

  Mahsa Tavakoli, Ruth Bankey, Cristi\'an Bravo · 26 juin 2026

  Enhancing the analysis of service feedback is essential for public sector organizations, particularly tax administrations, where trust and compliance depend on fair and effective service delivery. As feedback volumes grow, identifying emerging service quality issues and potential disparities across …

• Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model

  Abrar Alotaibi, Atta-ur Rahman, Raheel Alhaza, Wala Alkhalifa, Narjes Alhajjaj, Atheer Alharthi, Dhai Abushoumi, Maryam Alqahtani, Dania Alkhulaifi · 25 juin 2026

  Saudi Telecom Company (STC) is among the most popular companies in Saudi Arabia, with many customers. Yet, there is still a big room for improvement in users' satisfaction. Social media is the most robust platform to gauge users' satisfaction and determine their sentiments and critics. Twitter is am…

• Evaluating LLM Usage for Efficient and Explainable Numerical and Classified Implicit Sentiment Analysis of Product Desirability

  Sherri Weitl-Harms, John Hastings · 24 juin 2026

  Qualitative product feedback can reveal nuanced user experiences, but its implicit sentiment is difficult to measure. This paper presents a scalable and interpretable framework that uses large language models (LLMs) to quantify product desirability from such data. Using two Product Desirability Tool…

• Using machine learning to build public policy agenda from social media conversations

  Rahman Sanya · 23 juin 2026

  Issue identification and agenda setting represents an important stage in the public policy making process. Traditional approaches for carrying out activities under this stage are time- and labor-intensive on data collection and analysis, in addition to being costly to scale over large geographic are…

• The Register Gap: A Meaning Intelligence Framework for Nigerian Public Discourse

  Celestine Achi · 19 juin 2026

  We introduce the Meaning Intelligence Framework (MIF), a nine-dimension annotation and evaluation schema for Nigerian public discourse that separates surface sentiment from true communicative intent. Existing benchmarks for Nigerian languages, including NaijaSenti and AfriSenti, treat sentiment clas…

• Robust Dual-Signal Fusion: Hybrid Neuro-Symbolic Gating with Compressed Chain-of-Thought Refinement for Irony Detection in Social Media Texts

  Ankit Bhattacharjee, Krityapriya Bhaumik · 16 juin 2026

  Large Language Models (LLMs) natively default to literal semantic interpretations, making zero-shot irony detection a persistent challenge. We introduce the Robust Dual-Signal (RDS) Fusion framework, a hybrid neuro-symbolic architecture that compresses Chain-of-Thought (CoT) reasoning trajectories w…