Sentiment Analysis and Opinion Mining

Latest papers

• Aspect Extraction from E-Commerce Product and Service Reviews

  Valiant Lance D. Dionela, Fatima Kriselle S. Dy, Robin James M. Hombrebueno, Aaron Rae M. Nicolas, Charibeth K. Cheng, Raphael W. Gonda · 6 January 2026

  Aspect Extraction (AE) is a key task in Aspect-Based Sentiment Analysis (ABSA), yet it remains difficult to apply in low-resource and code-switched contexts like Taglish, a mix of Tagalog and English commonly used in Filipino e-commerce reviews. This paper introduces a comprehensive AE pipeline desi…

• Polarity Detection of Sustainable Development Goals in News Text

  Andrea Cadeddu, Alessandro Chessa, Vincenzo De Leo, Gianni Fenu, Francesco Osborne, Diego Reforgiato Recupero, Angelo Salatino, Luca Secchi · 6 January 2026

  The United Nations' Sustainable Development Goals (SDGs) provide a globally recognised framework for addressing critical societal, environmental, and economic challenges. Recent developments in natural language processing (NLP) and large language models (LLMs) have facilitated the automatic classifi…

• Measuring Social Media Polarization Using Large Language Models and Heuristic Rules

  Jawad Chowdhury, Rezaur Rashid, Gabriel Terejanu · 6 January 2026

  Understanding affective polarization in online discourse is crucial for evaluating the societal impact of social media interactions. This study presents a novel framework that leverages large language models (LLMs) and domain-informed heuristics to systematically analyze and quantify affective polar…

• ALF: Advertiser Large Foundation Model for Multi-Modal Advertiser Understanding

  Santosh Rajagopalan, Jonathan Vronsky, Songbai Yan, S. Alireza Golestaneh, Shubhra Chandra, Min Zhou · 1 January 2026

  We present ALF (Advertiser Large Foundation model), a multi-modal transformer architecture for understanding advertiser behavior and intent across text, image, video, and structured data modalities. Through contrastive learning and multi-task optimization, ALF creates unified advertiser representati…

• Introducing TrGLUE and SentiTurca: A Comprehensive Benchmark for Turkish General Language Understanding and Sentiment Analysis

  Duygu Altinok · 29 December 2025

  Evaluating the performance of various model architectures, such as transformers, large language models (LLMs), and other NLP systems, requires comprehensive benchmarks that measure performance across multiple dimensions. Among these, the evaluation of natural language understanding (NLU) is particul…

• Zero-Training Temporal Drift Detection for Transformer Sentiment Models: A Comprehensive Analysis on Authentic Social Media Streams

  Aayam Bansal, Ishaan Gangwani · 25 December 2025

  We present a comprehensive zero-training temporal drift analysis of transformer-based sentiment models validated on authentic social media data from major real-world events. Through systematic evaluation across three transformer architectures and rigorous statistical validation on 12,279 authentic s…

• From Fake Focus to Real Precision: Confusion-Driven Adversarial Attention Learning in Transformers

  Yawei Liu · 25 December 2025

  Transformer-based models have been widely adopted for sentiment analysis tasks due to their exceptional ability to capture contextual information. However, these methods often exhibit suboptimal accuracy in certain scenarios. By analyzing their attention distributions, we observe that existing model…

• Confidence-Credibility Aware Weighted Ensembles of Small LLMs Outperform Large LLMs in Emotion Detection

  Menna Elgabry, Ali Hamdi · 22 December 2025

  This paper introduces a confidence-weighted, credibility-aware ensemble framework for text-based emotion detection, inspired by Condorcet's Jury Theorem (CJT). Unlike conventional ensembles that often rely on homogeneous architectures, our approach combines architecturally diverse small transformer-…

• Multi-Modality Collaborative Learning for Sentiment Analysis

  Shanmin Wang, Chengguang Liu, Qingshan Liu · 19 December 2025

  Multimodal sentiment analysis (MSA) identifies individuals' sentiment states in videos by integrating visual, audio, and text modalities. Despite progress in existing methods, the inherent modality heterogeneity limits the effective capture of interactive sentiment features across modalities. In thi…

• Social Story Frames: Contextual Reasoning about Narrative Intent and Reception

  Joel Mire, Maria Antoniak, Steven R. Wilson, Zexin Ma, Achyutarama R. Ganti, Andrew Piper, Maarten Sap · 19 December 2025

  Reading stories evokes rich interpretive, affective, and evaluative responses, such as inferences about narrative intent or judgments about characters. Yet, computational models of reader response are limited, preventing nuanced analyses. To address this gap, we introduce SocialStoryFrames, a formal…

• When a Nation Speaks: Machine Learning and NLP in People's Sentiment Analysis During Bangladesh's 2024 Mass Uprising

  Md. Samiul Alim, Mahir Shahriar Tamim, Maisha Rahman, Tanvir Ahmed Khan, Md Mushfique Anwar · 18 December 2025

  Sentiment analysis, an emerging research area within natural language processing (NLP), has primarily been explored in contexts like elections and social media trends, but there remains a significant gap in understanding emotional dynamics during civil unrest, particularly in the Bangla language. Ou…

• Applying NLP to iMessages: Understanding Topic Avoidance, Responsiveness, and Sentiment

  Alan Gerber, Sam Cooperman · 15 December 2025

  What is your messaging data used for? While many users do not often think about the information companies can gather based off of their messaging platform of choice, it is nonetheless important to consider as society increasingly relies on short-form electronic communication. While most companies ke…

• Enhanced Sentiment Interpretation via a Lexicon-Fuzzy-Transformer Framework

  Shayan Rokhva, Mousa Alizadeh, Maryam Abdollahi Shamami · 11 December 2025

  Accurately detecting sentiment polarity and intensity in product reviews and social media posts remains challenging due to informal and domain-specific language. To address this, we propose a novel hybrid lexicon-fuzzy-transformer framework that combines rule-based heuristics, contextual deep learni…

• COINS: SemantiC Ids Enhanced COLd Item RepresentatioN for Click-through Rate Prediction in E-commerce Search

  Qihang Zhao, Zhongbo Sun, Xiaoyang Zheng, Xian Guo, Siyuan Wang, Zihan Liang, Mingcan Peng, Ben Chen, Chenyi Lei · 11 December 2025

  With the rise of modern search and recommendation platforms, insufficient collaborative information of cold-start items exacerbates the Matthew effect of existing platform items, challenging platform diversity and becoming a longstanding issue. Existing methods align items' side content with collabo…

• COINS: SemantiC Ids Enhanced COLd Item RepresentatioN for Click-through Rate Prediction in E-commerce Sarch

  Qihang Zhao, Zhongbo Sun, Xiaoyang Zheng, Xian Guo, Siyuan Wang, Zihan Liang, Mingcan Peng, Ben Chen, Chenyi Lei · 10 December 2025

  With the rise of modern search and recommendation platforms, insufficient collaborative information of cold-start items exacerbates the Matthew effect of existing platform items, challenging platform diversity and becoming a longstanding issue. Existing methods align items' side content with collabo…

• MIDG: Mixture of Invariant Experts with knowledge injection for Domain Generalization in Multimodal Sentiment Analysis

  Yangle Li, Danli Luo, Haifeng Hu · 9 December 2025

  Existing methods in domain generalization for Multimodal Sentiment Analysis (MSA) often overlook inter-modal synergies during invariant features extraction, which prevents the accurate capture of the rich semantic information within multimodal data. Additionally, while knowledge injection techniques…

• DashFusion: Dual-stream Alignment with Hierarchical Bottleneck Fusion for Multimodal Sentiment Analysis

  Yuhua Wen, Qifei Li, Yingying Zhou, Yingming Gao, Zhengqi Wen, Jianhua Tao, Ya Li · 8 December 2025

  Multimodal sentiment analysis (MSA) integrates various modalities, such as text, image, and audio, to provide a more comprehensive understanding of sentiment. However, effective MSA is challenged by alignment and fusion issues. Alignment requires synchronizing both temporal and semantic information …

• Public Sentiment Analysis of Traffic Management Policies in Knoxville: A Social Media Driven Study

  Shampa Saha, Shovan Roy · 4 December 2025

  This study presents a comprehensive analysis of public sentiment toward traffic management policies in Knoxville, Tennessee, utilizing social media data from Twitter and Reddit platforms. We collected and analyzed 7906 posts spanning January 2022 to December 2023, employing Valence Aware Dictionary …

• Enhancing BERT Fine-Tuning for Sentiment Analysis in Lower-Resourced Languages

  Jozef Kub\'ik, Marek \v{S}uppa, Martin Tak\'a\v{c} · 2 December 2025

  Limited data for low-resource languages typically yield weaker language models (LMs). Since pre-training is compute-intensive, it is more pragmatic to target improvements during fine-tuning. In this work, we examine the use of Active Learning (AL) methods augmented by structured data selection strat…

• PSA-MF: Personality-Sentiment Aligned Multi-Level Fusion for Multimodal Sentiment Analysis

  Heng Xie, Kang Zhu, Zhengqi Wen, Jianhua Tao, Xuefei Liu, Ruibo Fu, Changsheng Li · 2 December 2025

  Multimodal sentiment analysis (MSA) is a research field that recognizes human sentiments by combining textual, visual, and audio modalities. The main challenge lies in integrating sentiment-related information from different modalities, which typically arises during the unimodal feature extraction p…

• Sentiment Analysis and Emotion Classification using Machine Learning Techniques for Nagamese Language - A Low-resource Language

  Ekha Morang, Surhoni A. Ngullie, Sashienla Longkumer, Teisovi Angami · 2 December 2025

  The Nagamese language, a.k.a Naga Pidgin, is an Assamese-lexified creole language developed primarily as a means of communication in trade between the people from Nagaland and people from Assam in the north-east India. Substantial amount of work in sentiment analysis has been done for resource-rich …

• BanglaSentNet: An Explainable Hybrid Deep Learning Framework for Multi-Aspect Sentiment Analysis with Cross-Domain Transfer Learning

  Ariful Islam, Md Rifat Hossen, Tanvir Mahmud · 1 December 2025

  Multi-aspect sentiment analysis of Bangla e-commerce reviews remains challenging due to limited annotated datasets, morphological complexity, code-mixing phenomena, and domain shift issues, affecting 300 million Bangla-speaking users. Existing approaches lack explainability and cross-domain generali…

• Listwise Preference Optimization with Element-wise Confusions for Aspect Sentiment Quad Prediction

  Wenna Lai, Haoran Xie, Guandong Xu, Qing Li, S. Joe Qin · 1 December 2025

  Aspect sentiment quad prediction (ASQP) is inherently challenging to predict a structured quadruple with four core sentiment elements, including aspect term (a), aspect category (c), opinion term (o), and sentiment polarity (s). Prior methods relying on marker-based prediction struggle with modeling…

• BanglaASTE: A Novel Framework for Aspect-Sentiment-Opinion Extraction in Bangla E-commerce Reviews Using Ensemble Deep Learning

  Ariful Islam, Md Rifat Hossen, Abir Ahmed, B M Taslimul Haque · 27 November 2025

  Aspect-Based Sentiment Analysis (ABSA) has emerged as a critical tool for extracting fine-grained sentiment insights from user-generated content, particularly in e-commerce and social media domains. However, research on Bangla ABSA remains significantly underexplored due to the absence of comprehens…

• FITRep: Attention-Guided Item Representation via MLLMs

  Guoxiao Zhang, Ao Li, Tan Qu, Qianlong Xie, Xingxing Wang · 27 November 2025

  Online platforms usually suffer from user experience degradation due to near-duplicate items with similar visuals and text. While Multimodal Large Language Models (MLLMs) enable multimodal embedding, existing methods treat representations as black boxes, ignoring structural relationships (e.g., prim…