As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where
deep learning techniques may fail. It is widely applied in computer vision then introduced to …

Language models are ubiquitous in current NLP, and their multilingual capacity has recently
attracted considerable attention. However, current analyses have almost exclusively focused …

Abstract Analysis of subjective texts like offensive content or hate speech is a great
challenge, especially regarding annotation process. Most of current annotation procedures …

Fake news has become a major challenge for online platforms and society as a whole, with
potentially harmful consequences for individuals and organizations. While there has been a …

Sentiment analysis on social media relies on comprehending the natural language and
using a robust machine learning technique that learns multiple layers of representations or …

Sentiment analysis is an essential task in natural language processing that involves
identifying a text's polarity, whether it expresses positive, negative, or neutral sentiments …

Urdu is still considered a low-resource language despite being ranked as world's 10 th most
spoken language with nearly 230 million speakers. The scarcity of benchmark datasets in …

This report began life in October 2020 at the start of the Language In The Human-Machine
Era network (lithme. eu). Several online co-writing workshops followed, working together in …

This paper presents a detailed comparative study of the zero-shot cross-lingual sentiment
analysis. Namely, we use modern multilingual Transformer-based models and linear …

Target-dependent sentiment classification (TSC) enables a fine-grained automatic analysis
of sentiments expressed in texts. Sentiment expression varies depending on the domain …