Large language models (LLMs) have demonstrated impressive capabilities in natural
language processing. However, their internal mechanisms are still unclear and this lack of …

Stock movement prediction is an important field of study that can help market traders make
better trading decisions and earn more profit. The fusion of text from social media platforms …

Sentiment analysis is one of the traditional well-known tasks in Natural Language
Processing (NLP) research. In recent years, Pre-trained Models (PMs) have become one of …

Aspect-based sentiment analysis (ABSA) and Targeted ASBA (TABSA) allow finer-grained
inferences about sentiment to be drawn from the same text, depending on context. For …

BERT, as one of the pretrianed language models, attracts the most attention in recent years
for creating new benchmarks across GLUE tasks via fine-tuning. One pressing issue is to …

Attention mechanisms in deep neural network models have helped them to achieve
exceptional performance at complex natural language processing tasks. Previous attempts …

While attention is all you need may be proving true, we do not know why: attention-based
transformer models such as BERT are superior but how information flows from input tokens …

Although popular and effective, large language models (LLM) are characterised by a
performance vs. transparency trade-off that hinders their applicability to sensitive scenarios …

The expressive power and effectiveness of large language models (LLMs) is going to
increasingly push intelligent agents towards sub-symbolic models for natural language …

The main approaches to sentiment analysis are rule-based methods and machine learning,
in particular, deep neural network models with the Transformer architecture, including BERT …