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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.
This paper presents the construction of a Korean evaluation-annotated corpus (EVAD) for fine-grained aspect-based sentiment analysis in e-commerce reviews using Semi-Automatic Symbolic Propagation. It evaluates KoBERT and KcBERT models on the dataset, achieving high F1 scores in aspect-value pair recognition.
This paper introduces a multilingual dataset of over 100,000 movie reviews from Kazakhstan, containing Russian, Kazakh, and code-switched texts. It benchmarks classical and transformer-based models on sentiment polarity and score classification tasks.
This paper details the YEZE system for SemEval-2026 Task 9, which detects online polarization in 22 languages using a heterogeneous ensemble of XLM-RoBERTa and mDeBERTa models.
Researchers use three open-source LLMs to annotate 10,600 persuader turns in the PersuasionForGood corpus with 41 persuasion strategies, finding that strategy categories explain little donation variance and guilt induction significantly lowers donation rates.
Researchers from EPFL and Idiap apply NLP methods (topic modeling, sentiment analysis, readability scoring) to over 2000 hyper-local news articles to assess how well local French-language media serves migrant communities. The study combines focus groups with computational text analysis to identify gaps between local news content and migrant readers' needs.
A research paper presenting a dataset and XGBoost-based model for sentiment analysis of German Sign Language (DGS) fairy tales using facial and body motion features extracted via MediaPipe, achieving 63.1% balanced accuracy and demonstrating the importance of both facial and body movements for sentiment communication in sign language.
This paper presents SSAS (Syntactic & Semantic Context Assessment Summarization), a framework designed to improve consistency in LLM-based sentiment prediction by reducing noise and variance through hierarchical classification and iterative summarization. Empirical evaluation on three industry-standard datasets shows up to 30% improvement in data quality and reliability for enterprise decision-making.
This paper introduces a retrieval-augmented LLM framework for financial sentiment analysis, achieving 15-48% improvement in accuracy and F1 score over traditional models and LLMs like ChatGPT and LLaMA.
Yabble has introduced Yabble Count, an AI tool that analyzes customer feedback by categorizing sentiments and organizing unstructured data into themes to help businesses extract actionable insights from customer input.
OpenAI demonstrates an unsupervised system that learns sentiment representation by training a multiplicative LSTM to predict the next character in Amazon reviews, achieving state-of-the-art sentiment analysis on Stanford Sentiment Treebank (91.8% accuracy) while requiring 30-100x fewer labeled examples than supervised approaches. The model discovers a distinct 'sentiment neuron' that captures sentiment information and can be directly manipulated to control text generation sentiment.