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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.
This paper uses a BERT-based large language model for sentiment analysis of Decentraland's Discord community to enhance MANA token price prediction, demonstrating that a multi-modal LSTM incorporating sentiment, trading volume, and market capitalization outperforms a price-only baseline.
This paper presents a framework for Arabic financial sentiment analysis using LLMs, tailored for the Saudi market, integrating news and social media data to capture investor sentiment.
This paper proposes a generative framework for emotion intensity evaluation, shifting from discrete classification to continuous 0-100 scoring. It demonstrates superior performance and generalization in domains like finance.
Jenova AI launches a brand monitoring tool that searches across multiple platforms (Reddit, YouTube, X, LinkedIn, TikTok, Amazon, Google) in a single request, with smart keyword coverage, sentiment analysis, and competitive comparison.
The paper proposes a transformer-based model to predict political ideology of German political texts on a continuous left-to-right spectrum. The study compares 13 models and finds DeBERTa-large and Gemma2-2B perform best on different tasks.