Sentiment Analysis and Opinion Mining

Últimos artículos

• Language-Independent Sentiment Labelling with Distant Supervision: A Case Study for English, Sepedi and Setswana

  Koena Ronny Mabokela, Tim Schlippe, Mpho Raborife, Turgay Celik · 26 de noviembre de 2025

  Sentiment analysis is a helpful task to automatically analyse opinions and emotions on various topics in areas such as AI for Social Good, AI in Education or marketing. While many of the sentiment analysis systems are developed for English, many African languages are classified as low-resource langu…

• A Unified BERT-CNN-BiLSTM Framework for Simultaneous Headline Classification and Sentiment Analysis of Bangla News

  Mirza Raquib, Munazer Montasir Akash, Tawhid Ahmed, Saydul Akbar Murad, Farida Siddiqi Prity, Mohammad Amzad Hossain, Asif Pervez Polok, Nick Rahimi · 25 de noviembre de 2025

  In our daily lives, newspapers are an essential information source that impacts how the public talks about present-day issues. However, effectively navigating the vast amount of news content from different newspapers and online news portals can be challenging. Newspaper headlines with sentiment anal…

• Large Language Models for Sentiment Analysis to Detect Social Challenges: A Use Case with South African Languages

  Koena Ronny Mabokela, Tim Schlippe, Matthias W\"olfel · 24 de noviembre de 2025

  Sentiment analysis can aid in understanding people's opinions and emotions on social issues. In multilingual communities sentiment analysis systems can be used to quickly identify social challenges in social media posts, enabling government departments to detect and address these issues more precise…

• An Interpretability-Guided Framework for Responsible Synthetic Data Generation in Emotional Text

  Paula Joy B. Martinez, Jose Marie Antonio Mi\~noza, Sebastian C. Iba\~nez · 21 de noviembre de 2025

  Emotion recognition from social media is critical for understanding public sentiment, but accessing training data has become prohibitively expensive due to escalating API costs and platform restrictions. We introduce an interpretability-guided framework where Shapley Additive Explanations (SHAP) pro…

• MOON: Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understanding

  Daoze Zhang, Zhanheng Nie, Jianyu Liu, Chenghan Fu, Wanxian Guan, Yuan Gao, Jun Song, Pengjie Wang, Jian Xu, Bo Zheng · 19 de noviembre de 2025

  With the rapid advancement of e-commerce, exploring general representations rather than task-specific ones has attracted increasing research attention. For product understanding, although existing discriminative dual-flow architectures drive progress in this field, they inherently struggle to model …

• From Graphs to Hypergraphs: Enhancing Aspect-Based Sentiment Analysis via Multi-Level Relational Modeling

  Omkar Mahesh Kashyap, Padegal Amit, Madhav Kashyap, Ashwini M Joshi, Shylaja SS · 19 de noviembre de 2025

  Aspect-Based Sentiment Analysis (ABSA) predicts sentiment polarity for specific aspect terms, a task made difficult by conflicting sentiments across aspects and the sparse context of short texts. Prior graph-based approaches model only pairwise dependencies, forcing them to construct multiple graphs…

• Conditional Information Bottleneck for Multimodal Fusion: Overcoming Shortcut Learning in Sarcasm Detection

  Yihua Wang, Qi Jia, Cong Xu, Feiyu Chen, Yuhan Liu, Haotian Zhang, Liang Jin, Lu Liu, Zhichun Wang · 18 de noviembre de 2025

  Multimodal sarcasm detection is a complex task that requires distinguishing subtle complementary signals across modalities while filtering out irrelevant information. Many advanced methods rely on learning shortcuts from datasets rather than extracting intended sarcasm-related features. However, our…

• Classification of Hope in Textual Data using Transformer-Based Models

  Chukwuebuka Fortunate Ijezue, Tania-Amanda Fredrick Eneye, Maaz Amjad · 18 de noviembre de 2025

  This paper presents a transformer-based approach for classifying hope expressions in text. We developed and compared three architectures (BERT, GPT-2, and DeBERTa) for both binary classification (Hope vs. Not Hope) and multiclass categorization (five hope-related categories). Our initial BERT implem…

• Synergistic Feature Fusion for Latent Lyrical Classification: A Gated Deep Learning Architecture

  M. A. Gameiro · 18 de noviembre de 2025

  This study addresses the challenge of integrating complex, high-dimensional deep semantic features with simple, interpretable structural cues for lyrical content classification. We introduce a novel Synergistic Fusion Layer (SFL) architecture, a deep learning model utilizing a gated mechanism to mod…

• Emotion Detection From Social Media Posts

  Md Mahbubur Rahman, Shaila Sharmin · 6 de noviembre de 2025

  Over the last few years, social media has evolved into a medium for expressing personal views, emotions, and even business and political proposals, recommendations, and advertisements. We address the topic of identifying emotions from text data obtained from social media posts like Twitter in this r…

• Data-Efficient Adaptation and a Novel Evaluation Method for Aspect-based Sentiment Analysis

  Yan Cathy Hua, Paul Denny, J\"org Wicker, Katerina Ta\v{s}kova · 6 de noviembre de 2025

  Aspect-based Sentiment Analysis (ABSA) is a fine-grained opinion mining approach that identifies and classifies opinions associated with specific entities (aspects) or their categories within a sentence. Despite its rapid growth and broad potential, ABSA research and resources remain concentrated in…

• Solving cold start in news recommendations: a RippleNet-based system for large scale media outlet

  Karol Radziszewski, Micha{\l} Szpunar, Piotr Ociepka, Mateusz Buczy\'nski · 5 de noviembre de 2025

  We present a scalable recommender system implementation based on RippleNet, tailored for the media domain with a production deployment in Onet.pl, one of Poland's largest online media platforms. Our solution addresses the cold-start problem for newly published content by integrating content-based it…

• Multi-refined Feature Enhanced Sentiment Analysis Using Contextual Instruction

  Peter Atandoh, Jie Zou, Weikang Guo, Jiwei Wei, Zheng Wang · 4 de noviembre de 2025

  Sentiment analysis using deep learning and pre-trained language models (PLMs) has gained significant traction due to their ability to capture rich contextual representations. However, existing approaches often underperform in scenarios involving nuanced emotional cues, domain shifts, and imbalanced …

• Enhancing Sentiment Classification with Machine Learning and Combinatorial Fusion

  Sean Patten, Pin-Yu Chen, Christina Schweikert, D. Frank Hsu · 3 de noviembre de 2025

  This paper presents a novel approach to sentiment classification using the application of Combinatorial Fusion Analysis (CFA) to integrate an ensemble of diverse machine learning models, achieving state-of-the-art accuracy on the IMDB sentiment analysis dataset of 97.072\%. CFA leverages the concept…

• An Enhanced Dual Transformer Contrastive Network for Multimodal Sentiment Analysis

  Phuong Q. Dao, Mark Roantree, Vuong M. Ngo · 29 de octubre de 2025

  Multimodal Sentiment Analysis (MSA) seeks to understand human emotions by jointly analyzing data from multiple modalities typically text and images offering a richer and more accurate interpretation than unimodal approaches. In this paper, we first propose BERT-ViT-EF, a novel model that combines po…

• LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation of Likert Ratings

  Benjamin F. Maier, Ulf Aslak, Luca Fiaschi, Nina Rismal, Kemble Fletcher, Christian C. Luhmann, Robbie Dow, Kli Pappas, Thomas V. Wiecki · 28 de octubre de 2025

  Consumer research costs companies billions annually yet suffers from panel biases and limited scale. Large language models (LLMs) offer an alternative by simulating synthetic consumers, but produce unrealistic response distributions when asked directly for numerical ratings. We present semantic simi…

• Multilingual Target-Stance Extraction

  Ethan Mines, Bonnie Dorr · 28 de octubre de 2025

  Social media enables data-driven analysis of public opinion on contested issues. Target-Stance Extraction (TSE) is the task of identifying the target discussed in a document and the document's stance towards that target. Many works classify stance towards a given target in a multilingual setting, bu…

• Opinion Mining Based Entity Ranking using Fuzzy Logic Algorithmic Approach

  Pratik N. Kalamkar, A. G. Phakatkar · 28 de octubre de 2025

  Opinions are central to almost all human activities and are key influencers of our behaviors. In current times due to growth of social networking website and increase in number of e-commerce site huge amount of opinions are now available on web. Given a set of evaluative statements that contain opin…