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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.
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.
An AI tool that will soon be open-source, using DeepSeek to automatically fetch AppStore user reviews and perform information mining, helping product managers understand user feedback, version issues, and product opportunities.
This paper presents a unified multi-modal framework integrating reinforcement learning, high-frequency trading, game-theoretic approaches, and cross-modal sentiment analysis for intelligent financial systems, claiming significant improvements over single-domain systems.
This paper investigates whether topic sentiment causally affects perceived political ideology in news articles, comparing human annotations from AllSides with those from LLMs including GPT-4o-mini and Llama-3.3-70B. It finds that fine-tuned GPT-4o-mini exhibits a spurious sentiment-ideology coupling not present in human judgments, highlighting risks of using LLM annotations as proxies in causal analyses.
This paper introduces a text-based causal inference methodology using an enhanced CausalBERT to disentangle the effects of individual aspects (e.g., school administration, academic performance) on overall online review ratings, validated on 600K+ U.S. K-12 school reviews. Key improvements include temperature scaling, hyperparameter optimization, and interpretability methods to reduce confounding bias.
ACAT is a web-based collaborative annotation platform supporting four Aspect-Based Sentiment Analysis (ABSA) workflows, featuring an automated ETL pipeline that computes Inter-Annotator Agreement metrics at export to produce training-ready datasets. Validated on 1,002 restaurant reviews, it achieves a median annotation time of 31.58 seconds and raw IAA up to 0.86.
A large-scale dataset of 299,329 public Facebook posts about climate change, with metadata and analysis of themes and engagement, aimed at supporting research on climate discourse.
Introduces GHI, a Graphormer-over-conditioned-hypergraph-incidence framework for aspect-based sentiment analysis that represents linguistic evidence as token–hyperedge incidence relations, achieving state-of-the-art results on six benchmarks with only 247M parameters.
This paper compares multiple machine learning and transformer models for sentiment classification on movie reviews, finding RoBERTa achieves 93.02% accuracy, and a soft voting ensemble improves performance.