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

• Human-Based Machine Translation Evaluation: A Multi-Dimensional Approach to Sentiment, Emotion, and Argumentation Preservation in Chinese-English Translation
  Jingshi Zhou · Open MIND · Jan 1, 2028

  The research landscape of Machine Translation Evaluation (MTE) has traditionally been dominated by automated metrics that, while computationally efficient, often fail to capture the nuanced aspects of translation quality paramount to human …

• Mapping emotions in bad religion’s music : insights from qualitative and automatic sentiment analysis
  Catherine Bouko, Luna De Bruyne · Ghent University Academic B... · Jan 1, 2027

  In this chapter, we analysed Bad Religion’s seventeen studio albums’ emotional expressions using automatic sentiment analysis to map the emotional trends over time, and to evaluate how these emotions are manifested. The automatic sentiment …

• Sentiment Analysis Performance of Lora-Enhanced Llm's
  Tunahan TİMUÇİN · Open MIND · Dec 11, 2026

  On social media platforms, the increase in the number of users and the resulting increase in thedata produced by users have accelerated the prominence of some technologies. The most well-known of these areas is Natural Language Processing (…

• Sentiment Analysis Performance of Lora-Enhanced Llm's
  Tunahan TİMUÇİN · Zenodo (CERN European Organ... · Dec 11, 2026

  On social media platforms, the increase in the number of users and the resulting increase in thedata produced by users have accelerated the prominence of some technologies. The most well-known of these areas is Natural Language Processing (…

• "None of This Is Normal": The Golden Age of Legal Bribery
  James J. Sample · eYLS (Yale Law School) · Dec 1, 2026

  John Adams, in one of his most-quoted turns of phrase, remarked that the "very definition of a Republic, is 'an Empire of laws, not of men.'" 1 As citizens and as lawyers, we endorse the aspirations of Adams's sentiment, and especially so w…

• REGARD: Regional Affective Differences in Large Language Models
  Andrei Chetvergov, Alexander Evseev, Mikhail Solovev, Timofei Sivoraksha et al. · arXiv · Jul 22, 2026

  Large language models trained and aligned within different linguistic and regional ecosystems may frame the same political, cultural, and geopolitical entities in different ways. Such differences are often evaluated through sentiment, favor…

• Language-Specific versus Cross-Lingual Knowledge Graphs for Implicit Aspect Identification in Arabic: A Comparative Study of Reasoning and Adaptation Strategies
  Lujain A. Alawwad · arXiv · Jul 22, 2026

  Aspect-based sentiment analysis (ABSA) in Arabic must recover both explicitly stated aspects and implicit aspects that are never named in the text. Implicit identification typically relies on an auxiliary knowledge source (e.g., a knowledge…

• TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis
  Isabel Xu, Cynthia Xu, Rachel Ren, Cong Guo et al. · arXiv · Jul 22, 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 mu…

• Task Competence Is Not Instruction Following: Evaluating Instruction-Conflicting Behavior in Small Language Models
  Mahdiyeh Farajidizaji, Vatsal Raina · arXiv · Jul 21, 2026

  Instruction tuning is meant to make language models follow user requests, yet it is unclear whether small models comply when an instruction conflicts with their usual task behavior. We study this across three tasks - multiple-choice questio…

• Constraint-Aware Counterfactual Editing for Aspect-Based Sentiment Analysis
  S M Rafiuddin, Vamsi Krishna Pavuluri, Atriya Sen · arXiv · Jul 15, 2026

  Aspect-Based Sentiment Analysis (ABSA) requires models to identify sentiment toward specific aspects rather than relying on the global polarity of a sentence. This makes counterfactual evaluation especially challenging: a valid counterfactu…

• Segregate, Refine, Integrate: Decomposing Multimodal Fusion for Sentiment Analysis
  Alexios Filippakopoulos, Elias Kallioras, Nikolaos Xiros, Efthymios Georgiou et al. · arXiv · Jul 14, 2026

  Multimodal fusion must simultaneously refine modality-specific signals and model cross-modal interactions; two competing objectives typically entangled within the same operation. We propose \textbf{SeRIn} (\textbf{Se}gregate, \textbf{R}efin…

• A Durability and Cross-Language Transfer Benchmark for a Validated Teaching-Feedback Classification Protocol
  Esteban U. Vega Barajas · arXiv · Jul 13, 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, …

• MASTE: A Multi-Agent Pipeline for Zero-Shot Aspect Sentiment Triplet Extraction
  Ao Hong, Lehang Wang, Zhirun Yue, Mingxin Wang et al. · arXiv · Jul 9, 2026

  Aspect Sentiment Triplet Extraction (ASTE) requires jointly identifying (aspect, opinion, sentiment) triples from a given review sentence. While large language models (LLMs) achieve strong zero-shot performance on many NLP benchmarks, their…

• Unveiling Public Opinion: A Study of Sentiment Analysis Using LSTM and Traditional Models
  Atiq Ur Rehman · arXiv · Jul 8, 2026

  In this age of social media, sites like Twitter have become meeting places for people to share their views and feelings on a wide range of issues and current events as they unfold in real time. Sentiment analysis, a critical application of …

• Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
  Tianyang Liu, Canwen Xu, Fangyu Lei, Nikki Lijing Kuang et al. · arXiv · Jul 7, 2026

  Major cloud data platforms now expose large language model capabilities as native SQL functions, enabling analysts to perform classification, filtering, sentiment analysis, extraction, similarity search, and aggregation within ordinary SQL …

• Is Domain Adaptation Always Helpful? A Frozen-Backbone Study of Cross-Domain Sentiment Transfer
  Phat Tran, Artin Lahni, Pranav Kulkarni, Yaolun Zhang · arXiv · Jul 7, 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-do…

• Audio Sentiment Analysis via Distillation and Cross-Modal Integration of Generated Multilingual Transcripts
  Andrei-George Durdun, Victor Constantinescu, Radu Tudor Ionescu · arXiv · Jul 7, 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 …

• SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis
  Lakshani Galwatta, Nisansa de Silva, Sarangi Aththanayake, Adithya Galwatta · arXiv · Jul 6, 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…

• Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent
  Lei Bai, Zongsheng Cao, Yang Chen, Zhiyao Cui et al. · arXiv · Jun 29, 2026

  We introduce Agents-A1, a 35B Mixture-of-Experts Agentic Model that reaches trillion-parameter-level performance by scaling the agent horizon. We investigate agent-horizon scaling from two perspectives: scaling long-horizon trajectories and…

• Do We Still Need Fine Tuning? Turkish Sentiment Analysis in the Era of Large Language Model
  Sercan Karakaş, Yusuf Şimşek · arXiv · Jun 28, 2026

  This study examines whether supervised fine-tuning remains necessary for Turkish sentiment analysis in the era of large language models. We compare classical machine learning methods, fine-tuned pretrained language models, and prompted larg…

• Fault of Our Stars: Behavioral Drivers of Rating-Sentiment Incongruence
  Ramanaish Abaiyan, Ruththiragayan Sutharsan, Kusal Amantha, Anusan Krishnathas et al. · arXiv · Jun 24, 2026

  When people share experiences online, they often express thoughts in two ways: a star rating and a written review. In sentiment analysis, ratings are widely used as convenient weak labels for textual sentiment, yet whether the two actually …

• Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model
  Abrar Alotaibi, Atta-ur Rahman, Raheel Alhaza, Wala Alkhalifa et al. · arXiv · Jun 24, 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' satisfacti…

• NatureBench: Can Coding Agents Match the Published SOTA of Nature-Family Papers?
  Yuru Wang, Lejun Cheng, Yuxin Zuo, Sihang Zeng et al. · arXiv · Jun 23, 2026

  We introduce NatureBench, a cross-discipline benchmark of 90 tasks distilled from peer-reviewed Nature-family publications, designed to evaluate whether AI coding agents can move beyond reproduction toward discovery on real scientific probl…

• Aspect-Based Sentiment Evolution and its Correlation with Review Rounds in Multi-Round Peer Reviews: A Deep Learning Approach
  Ruxue Hana, Haomin Zhoua, Jiangtao Zhong, Chengzhi Zhang · arXiv · Jun 23, 2026

  Mining sentiment information from the textual content of peer review comments offers valuable insights into the scientific evaluation process. However, previous studies are often constrained by coarse-grained analysis and the lack of differ…

• Best Preprocessing Techniques for Sentiment Analysis
  Saranzaya Magsarjav, Melissa Humphries, Jonathan Tuke, Lewis Mitchell · arXiv · Jun 23, 2026

  Sentiment analysis in Twitter datasets is important because it enables monitoring public opinion on products and analysis of political and social movements. One critical step is preprocessing: the automated processing of text for machine le…

• TriggerBench: Investigating Prospective Memory for Large Language Models
  Tianhua Zhang, Xinjiang Wang, Qianxi Zhang, Qi Chen et al. · arXiv · Jun 22, 2026

  While Large Language Models (LLMs) are increasingly deployed in long interactions, existing evaluations focus predominantly on retrospective memory (RM) via explicit queries. Prospective memory (PM), the critical ability to spontaneously re…

• Language-Specific Sentiment Polarity Biases in Encoder and Large Language Model Classification of Product Reviews
  Advita Rajiv, Kavitha Kothur, Gautham Reddy · arXiv · Jun 22, 2026

  This study investigates sentiment polarity biases, specifically, differences in how accurately AI models classify positive versus negative reviews across languages and model architectures. Large language models show a negative bias in Frenc…

• The Register Gap: A Meaning Intelligence Framework for Nigerian Public Discourse
  Celestine Achi · arXiv · Jun 18, 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 languag…

• What sentiment analysis can't see: Measuring whether customers were helped, and what went wrong, across 70,000 support conversations
  Jason Potteiger · arXiv · Jun 18, 2026

  Most companies read their customer support data at scale using sentiment analysis, which measures how customers sound rather than whether they were satisfied with the result. We tested a richer alternative on 70,450 support conversations fr…

• Efficient Financial Language Understanding via Distillation with Synthetic Data
  Wen-Fong, Huang, Edwin Simpson · arXiv · Jun 17, 2026

  Large instruction-following models are powerful but costly to deploy, particularly in finance, where labelled data are limited by confidentiality and expert annotation cost. We present an efficient framework for financial sentiment analysis…