Sentiment analysis is one of the most widely studied applications in NLP, but most work
focuses on languages with large amounts of data. We introduce the first large-scale human …

The field of low-resource sentiment analysis has seen significant developments in recent
years. This research review SLR evaluates the approaches and data sources utilized in low …

Multi-modal Machine Translation (MMT) enables the use of visual information to enhance
the quality of translations. The visual information can serve as a valuable piece of context …

Accurate sentiment analysis is greatly hindered by the code-switching phenomena,
especially in the setting low resource language such as the Hausa. However, the majority of …

We describe our contribution to the SemEVAl 2023 AfriSenti-SemEval shared task, where
we tackle the task of sentiment analysis in 14 different African languages. We develop both …

Hausa is a major Chadic language, spoken by over 100 million people in Africa. However,
from a computational linguistic perspective, it is considered a low-resource language, with …

Aspect-based Sentiment Analysis (ABSA) is crucial for understanding sentiment nuances in
text, especially across diverse languages and cultures. This paper introduces a novel Deep …

The analysis of sentiments expressed on social media platforms is a crucial tool for
understanding user opinions and preferences. The large amount of the texts found on social …

We present the findings of SemEval-2023 Task 12, a shared task on sentiment analysis for
low-resource African languages using Twitter dataset. The task featured three subtasks; …

One of the most extensively researched applications in natural language processing (NLP)
is sentiment analysis. While the majority of the study focuses on high-resource languages …