Opinion Classification with Kili and HuggingFace AutoTrain

Huggy Lingo: Using Machine Learning to Improve Language Metadata on the Hugging Face Hub

Unsupervised sentiment neuron

We’ve developed an unsupervised system which learns an excellent representation of sentiment, despite being trained only to predict the next character in the text of Amazon reviews.

MONOVAB : An Annotated Corpus for Bangla Multi-label Emotion Detection

arXiv:2309.15670v3 Announce Type: replace Abstract: In recent years, Sentiment Analysis (SA) and Emotion Recognition (ER) have been increasingly popular in the Bangla language, which is the seventh m...

SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis

arXiv:2607. 05259v1 Announce Type: cross Abstract: 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.

Databricks ❤️ Hugging Face: up to 40% faster training and tuning of Large Language Models

huggingface_hub v1.0: Five Years of Building the Foundation of Open Machine Learning

HelaBERT: Enhancing Sinhala Language Understanding with Dual Pooling Classification Head

arXiv:2608.22922v1 Announce Type: new Abstract: We present HelaBERT, a family of two BERT-based masked language models pre-trained from scratch on approximately 1 billion tokens of Sinhala text sourc...

Concept Direction Reliability Across Languages with Different Tokenizer Fertility

arXiv:2609.36194v1 Announce Type: new Abstract: Extracted sentiment directions can vary across samples even when downstream sentiment classification remains accurate. To evaluate direction reproducib...

Towards Efficient Multimodal and Multilingual Opinion Extraction for STI: A QLoRA-Based Fine-Tuning Approach

arXiv:2608. 14152v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have reshaped semantic analysis.

Towards Efficient Multimodal and Multilingual Opinion Extraction for STI: A QLoRA-Based Fine-Tuning Approach

Recent advances in large language models (LLMs) have reshaped semantic analysis. Opinion Extraction (OE) for Science and Technology Intelligence (STI) requires concise core opinions from large information streams.

Do We Still Need Fine Tuning? Turkish Sentiment Analysis in the Era of Large Language Model

arXiv:2606. 29614v1 Announce Type: cross Abstract: This study examines whether supervised fine-tuning remains necessary for Turkish sentiment analysis in the era of large language models.

ViTOED: A Dataset for Target-Oriented Emotion Detection on Vietnamese Social Media Texts

This paper introduces ViTOED, a novel dataset for target-oriented emotion detection in Vietnamese social media texts. The ViTOED comprises 10,985 user comments and 21,244 manually annotated opinion quadruples (source, target, expression, polarity) that follow strict guidelines.