sentiment-analysis

This paper challenges the conventional repair-first paradigm for missing modalities in multimodal sentiment analysis, showing that full-modality input is only optimal for a small fraction of samples. The authors propose SIEVE, a plug-and-play method that learns sample-level decisions on whether to repair missing modalities, consistently improving existing repair backbones.

A research paper proposing SeRIn, a multimodal fusion scheme that separates modality-specific refinement from cross-modal interaction, achieving state-of-the-art on CH-SIMS and CMU-MOSEI benchmarks for sentiment analysis.

MASTE is a four-stage multi-agent pipeline for zero-shot aspect sentiment triplet extraction that outperforms zero-shot and chain-of-thought baselines without using labeled data.

This paper presents a comparative study of LSTM and traditional models for sentiment analysis of public opinion.

This paper proposes SHAP-weighted cross-modal expert fusion (XGAF) for emotion and sentiment recognition, demonstrating that sum-abs SHAP aggregation achieves early-fusion-level performance on MELD and CMU-MOSEI datasets.

This paper investigates whether explicit domain adaptation methods are beneficial for sentiment transfer when using frozen pre-trained language model backbones, finding that effectiveness depends on whether the backbone already possesses target-domain knowledge.

This paper presents methods for SemEval-2026 Task 3, using transformer ensembles and LLM-generated annotations to predict continuous valence and arousal scores in dimensional aspect-based sentiment analysis.

This paper evaluates twelve recent text encoders on their ability to encode affective cues from three psychological emotion theories, finding that instruction-aware open-weight encoders match or exceed proprietary ones at word level, while task-tuned embeddings are superior at sentence level.

Mira is an AI-powered interview moderator that reads and responds to people's feelings during interviews.

Pieter Levels analyzes his blog stats and finds that negative content performs about 1.5x better than positive content, while curious content ranks second. He shares detailed sentiment and emotion data from his 743 posts.

This paper proposes a leakage-safe diagnostic to test whether quality-aware multimodal fusion methods actually use reliability scores during inference, by permuting these scores across test examples. Experiments on StressID and CMU-MOSEI show that shuffled reliability scores leave performance unchanged, indicating that quality signals only influence decisions when they reliably predict unimodal correctness.

This paper investigates the behavioral drivers of incongruence between star ratings and textual sentiment in Sri Lankan tourism reviews, finding that 18.6% of reviews show mismatch with six directional patterns, and identifying venue type, reviewer expertise, and temporal factors as contributors.

This paper presents a sentiment analysis and spam detection system for Arabic tweets using the MARBERT model, trained on a dataset of 24,513 tweets to improve customer service for Saudi Telecom Company.

This paper investigates the distribution and evolution of aspect-level sentiments in multi-round peer reviews from Nature Communications, using a deep learning approach (LCF-BERT-CDM) to achieve 82.65% Macro-F1, and finds that positive sentiment increases while negative sentiment decreases with more review rounds.

This paper systematically investigates the optimal order of preprocessing techniques for sentiment analysis on Twitter data, finding that tokenisation is most impactful and spelling correction least, with the best order being tokenisation, cleaning, stemming, then stopword removal.

This paper presents a scalable framework using LLMs for implicit sentiment analysis of product desirability from qualitative feedback, achieving up to 0.97 Pearson correlation and 94% accuracy while providing explanations, with GPT-4o-mini offering similar performance at 94% lower cost.

Presents a framework for financial sentiment analysis using distillation with synthetic data, transferring knowledge from a large teacher to compact student models, with clustering-based seed selection for efficient low-resource domain adaptation.

Correl8 AI is an MCP tool that lets AI agents directly report meaningful user feedback such as bugs, confusion, and feature requests, helping teams surface product signals without reviewing all chat logs.

Honestly is a tool that aggregates and presents honest opinions about your product from Reddit and TikTok discussions.

Built an AI pipeline that converts financial news into structured analysis including sentiment, risks, and opportunities, focusing on consistency through prompt engineering and validation.