Latest Sentiment Analysis Research Papers

The newest Sentiment Analysis papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Sentiment Analysis so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• Explicit Representation Alignment for Multimodal Sentiment Analysis
  Baode Wang, Ziming Wang, Huacan Wang, Ronghao Chen et al. · arXiv · Jun 8, 2026

  Multimodal affective analysis aims to understand human sentiment and emotion by jointly modeling heterogeneous modalities such as text and images. However, multimodal models often fail to consistently outperform strong text-only baselines, …

• Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles
  Upasana Chatterjee · arXiv · Jun 4, 2026

  We ask whether topic sentiment has a causal effect on perceived political ideology, and whether the answer depends on who assigns the ideology label. Using articles from AllSides, paired with shared sentiment annotations from Llama-3.3-70b-…

• Forgive or forget: Understanding the context of hate in audio retrieval systems
  Arghya Pal, Sailaja Rajanala, Raphael C. -W. Phan, Shekhar Nayak · arXiv · Jun 4, 2026

  Handling toxic retrieval in text-to-audio systems is challenging due to contextual dependencies. Existing strategies (e.g., rephrasing, summarization) risk altering intent or omitting details. We propose a post hoc causal debiasing framewor…

• Prompting Is All You Need: Multi-view Prompting Large Language Models for Aspect-Based Sentiment Analysis
  Nils Constantin Hellwig, Niklas Donhauser, Jakob Fehle, Udo Kruschwitz et al. · arXiv · May 27, 2026

  Recent work explored the capabilities of Large Language Models (LLMs) in Aspect-Based Sentiment Analysis (ABSA) through few-shot prompting, requiring substantially fewer annotated examples while achieving notable improvements over zero-shot…

• PolyGnosis 2.0: Enhancing LLM Reasoning via Agentic Harness Engineering for Polymarket and OSINT Insight Extraction
  Daren Wang, Hong Xu, Jiawen Xian · arXiv · May 25, 2026

  This paper introduces PolyGnosis 2.0, a pioneering multi-agent architecture designed to extract predictive intelligence by synthesizing Polymarket anomaly signals with global Open Source Intelligence (OSINT) streams, specifically Global Dat…

• A Controlled Synthetic Benchmark for Educational Aspect-Based Sentiment Analysis
  Yehudit Aperstein, Alexander Apartsin · arXiv · May 25, 2026

  Educational aspect-based sentiment analysis (ABSA) can support course improvement, but public aspect-labeled student feedback remains scarce because educational reviews are private, institution-specific, and expensive to annotate. This stud…

• Reducing Political Manipulation with Consistency Training
  Long Phan, Devin Kim, Alexander Pan, Alice Blair et al. · arXiv · May 21, 2026

  Large language models (LLMs) exhibit systematic political bias across a variety of sensitive contexts. We find that LLMs handle counterpart topics from opposing political sides asymmetrically. We refer to this phenomenon as covert political…

• GHI: Graphormer over Conditioned Hypergraph Incidence for Aspect-Based Sentiment Analysis
  Yu Du, Wenlong Zhu, Xingze Li, Chenglong Cao et al. · arXiv · May 21, 2026

  Aspect-based sentiment analysis (ABSA) requires models to bind sentiment evidence to the correct aspect, making it a natural testbed for fine-grained structural reasoning. We introduce GHI, a Graphormer-over-Conditioned-Hypergraph-Incidence…

• Universal Adversarial Triggers
  Benedict Florance Arockiaraj, Alexander Feng, Jianxiong Cai, Xiaoyu Cheng · arXiv · May 18, 2026

  Recent works have illustrated that modern NLP models trained for diverse tasks ranging from sentiment analysis to language generation succumb to universal adversarial attacks, a class of input-agnostic attacks where a common trigger sequenc…

• Correcting Selection Bias in Sparse User Feedback for Large Language Model Quality Estimation: A Multi-Agent Hierarchical Bayesian Approach
  Andrea Morandi, Mahesh Viswanathan · arXiv · May 12, 2026

  [Abridged] Production LLM deployments receive feedback from a non-random fraction of users: thumbs sit mostly in the tails of the satisfaction distribution, and a naive average over them can land 40-50 percentage points away from true syste…

• ICT-NLP at SemEval-2026 Task 3: Less Is More -- Multilingual Encoder with Joint Training and Adaptive Ensemble for Dimensional Aspect Sentiment Regression
  Liyuan Huang, Jiawei He, Wutao Shen, Lin Li et al. · arXiv · May 11, 2026

  This paper describes our system to SemEval-2026 Task 3 Track A Subtask 1 on Dimensional Aspect Sentiment Regression (DimASR). We propose a lightweight and resource-efficient system built entirely on multilingual pre-trained encoders, withou…

• DECO-MWE: building a linguistic resource of Korean multiword expressions for feature-based sentiment analysis
  Jaeho Han, Changhoe Hwang, Seongyong Choi, Gwanghoon Yoo et al. · arXiv · May 11, 2026

  This paper aims to construct a linguistic resource of Korean Multiword Expressions for Feature-Based Sentiment Analysis (FBSA): DECO-MWE. Dealing with multiword expressions (MWEs) has been a critical issue in FBSA since many constructs reve…

• A Comparative Analysis of Classical Machine Learning and Deep Learning Approaches for Sentiment Classification on IMDb Movie Reviews
  Erma Daniar Safitri, Lia Hana Ichisasmita, Citra Agustin, Luluk Muthoharoh et al. · arXiv · May 8, 2026

  This paper presents a comparative study of classical machine learning and deep learning methods for sentiment classification on the IMDb movie reviews dataset. The machine learning pipeline uses TF-IDF features and PyCaret AutoML to evaluat…

• Hybrid TF--IDF Logistic Regression and MLP Neural Baseline for Indonesian Three-Class Sentiment Analysis on Social Media Text
  Allya Nurul Islami Pasha, Eka Fidiya Putri, Luluk Muthoharoh, Ardika Satria et al. · arXiv · May 8, 2026

  This paper presents a compact three-class sentiment analysis study for Indonesian social media text. The task is formulated with positive, negative, and neutral outputs derived from a fine-grained emotion dataset. The proposed practical bas…

• SSP-based construction of evaluation-annotated data for fine-grained aspect-based sentiment analysis
  Suwon Choi, Shinwoo Kim, Changhoe Hwang, Gwanghoon Yoo et al. · COLING · May 8, 2026

  We report the construction of a Korean evaluation-annotated corpus, hereafter called 'Evaluation Annotated Dataset (EVAD)', and its use in Aspect-Based Sentiment Analysis (ABSA) extended in order to cover e-commerce reviews containing senti…

• A Comparative Analysis of Machine Learning and Deep Learning Models for Tweet Sentiment Classification: A Case Study on the Sentiment140 Dataset
  Vita Anggraini, Cintya Bella, Bastian, Luluk Muthoharoh et al. · arXiv · May 6, 2026

  The exponential growth of social media has created an urgent need for automated systems to analyze unstructured public sentiment in real time. This study compares a traditional Logistic Regression model using TF-IDF features with a deep lea…

• Sentiment Analysis and Customer Satisfaction Prediction on E-Commerce Platforms Based on YouTube Comments Using the XGBoost Algorithm
  Ridho Benedictus Togi Manik, Muhammad Aqil Ramadhan, Ihsan Maulana Yusuf, Luluk Muthoharoh et al. · arXiv · May 6, 2026

  The exponential expansion of digital commerce in Indonesia has significantly shifted consumer interactions toward video-centric social networks, particularly YouTube. Consequently, the sheer volume of unstructured, multi-contextual comments…

• Annotation Quality in Aspect-Based Sentiment Analysis: A Case Study Comparing Experts, Students, Crowdworkers, and Large Language Model
  Niklas Donhauser, Jakob Fehle, Nils Constantin Hellwig, Markus Weinberger et al. · arXiv · May 5, 2026

  Aspect-Based Sentiment Analysis (ABSA) enables fine-grained opinion analysis by identifying sentiments toward specific aspects or targets within a text. While ABSA has been widely studied for English, research on other languages such as Ger…

• Sentiment Analysis of Indonesian Spotify Reviews Using Machine Learning and BiLSTM
  Uliano Wilyam Purba, Andre Hadiman Rotua Parhusip, Sahid Maulana, Luluk Muthoharoh et al. · arXiv · May 5, 2026

  This paper benchmarks classical machine learning and deep learning approaches for three-class sentiment classification of Indonesian Spotify reviews. Using 100,000 scraped reviews and 70,155 cleaned samples, the study compares Support Vecto…

• A Comparison of Traditional Machine Learning Algorithms and LSTM-Based Deep Learning Models for Email Sentiment Analysis
  Virdio Samuel Saragih, Baruna Abirawa, Kartini Lovian Simbolon, Luluk Muthoharoh et al. · arXiv · May 5, 2026

  The rapid growth of electronic communication has necessitated more robust systems for email classification and sentiment detection. This study presents a comparative performance analysis between traditional machine learning algorithms and d…

• Benchmarking Logistic Regression, SVM, Naive Bayes, and IndoBERT Fine-Tuning for Sentiment Analysis on Indonesian Product Reviews
  Nabila Zakiyah Zahra, Salwa Farhanatussaidah, Nasywa Nur Afifah, Luluk Muthoharoh et al. · arXiv · May 5, 2026

  The exponential growth of e-commerce platforms in Indonesia has generated a massive volume of user-generated product reviews. Analyzing the sentiment of these reviews is critical for measuring customer satisfaction and identifying product i…

• Semantically Enriching Investor Micro-blogs for Opinion-Aware Emotion Analysis: A Practical Approach
  Gaurav Negi, Paul Buitelaar · arXiv · May 4, 2026

  While sentiment analysis is the staple of financial NLP, capturing the nuances of 'why' behind that sentiment remains a challenge. There have been attempts to address this by analysing investor emotions alongside sentiment; however, this do…

• Directed Social Regard: Surfacing Targeted Advocacy, Opposition, Aid, Harms, and Victimization in Online Media
  Scott Friedman, Ruta Wheelock, Sonja Schmer-Galunder, Drisana Iverson et al. · arXiv · May 1, 2026

  The language in online platforms, influence operations, and political rhetoric frequently directs a mix of pro-social sentiment (e.g., advocacy, helpfulness, compassion) and anti-social sentiment (e.g., threats, opposition, blame) at differ…

• Stable Behavior, Limited Variation: Persona Validity in LLM Agents for Urban Sentiment Perception
  Neemias B da Silva, Rodrigo Minetto, Daniel Silver, Thiago H Silva · arXiv · Apr 30, 2026

  Large Language Models (LLMs) are increasingly used as proxies for human perception in urban analysis, yet it remains unclear whether persona prompting produces meaningful and reproducible behavioral diversity. We investigate whether distinc…

• Sentiment Analysis of AI Adoption in Indonesian Higher Education Using Machine Learning and Transformer-Based Models
  Happy Syahrul Ramadhan, Ahmad Sahidin Akbar, Karin Yehezkiel Sinaga, Luluk Muthoharoh et al. · arXiv · Apr 30, 2026

  This study analyzes Indonesian student opinions on the adoption of artificial intelligence in higher education using two approaches: TF-IDF-based machine learning and Transformer-based deep learning. The dataset consists of 2,295 labeled sa…

• Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models
  Gongbo Zhang, Wen Wang, Ye Tian, Li Yuan · arXiv · Apr 29, 2026

  Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters for competitive performance. While existing distillation methods for dLLMs reduce inference…

• Zero-Shot to Full-Resource: Cross-lingual Transfer Strategies for Aspect-Based Sentiment Analysis
  Jakob Fehle, Nils Constantin Hellwig, Udo Kruschwitz, Christian Wolff · arXiv · Apr 29, 2026

  Aspect-based Sentiment Analysis (ABSA) extracts fine-grained opinions toward specific aspects within text but remains largely English-focused despite major advances in transformer-based and instruction-tuned models. This work presents a mul…

• DSIPA: Detecting LLM-Generated Texts via Sentiment-Invariant Patterns Divergence Analysis
  Siyuan Li, Aodu Wulianghai, Guangyan Li, Xi Lin et al. · arXiv · Apr 29, 2026

  The rapid advancement of large language models (LLMs) presents new security challenges, particularly in detecting machine-generated text used for misinformation, impersonation, and content forgery. Most existing detection approaches struggl…

• Classification of Public Opinion on the Free Nutritional Meal Program on YouTube Media Using the LSTM Method
  Berliana Enda Putri, Lisa Diani Amelia, Muhammad Zaky Zaiddan, Luluk Muthoharoh et al. · arXiv · Apr 29, 2026

  Public opinion towards the Free Nutritious Meal Program (MBG) on YouTube social media reflects diverse community responses. This study applies the Long Short-Term Memory (LSTM) method to classify sentiments from 7,733 YouTube comments. The …

• Benchmarking PyCaret AutoML Against BiLSTM for Fine-Grained Emotion Classification: A Comparative Study on 20-Class Emotion Detection
  Arya Muda Siregar, Arielva Simon Siahaan, Haikal Fransisko Simbolon, Luluk Muthoharoh et al. · arXiv · Apr 29, 2026

  Fine-grained emotion classification, which identifies specific emotional states such as happiness, anger, sadness, and fear, remains a challenging task in natural language processing. This study benchmarks classical machine learning and dee…