Abstract Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states
in texts. Various Deep Learning (DL) methods have developed rapidly, and they have …

Traditional methods for sentiment analysis, when applied in a monolingual context, often
yield less than optimal results in multilingual settings. This underscores the need for a more …

Multi-label text classification (MLTC) involves tagging a document with its most relevant
subset of labels from a label set. In real applications, labels usually follow a long-tailed …

Depression is increasingly prevalent, leading to higher suicide risk. Depression detection
and sentimental analysis of text inputs in cross-domain frameworks are challenging. Solo …

Many natural language processing (NLP) tasks are naturally imbalanced, as some target
categories occur much more frequently than others in the real world. In such scenarios …

The proliferation of network attacks poses a significant threat. Researchers propose datasets
for network attacks to support research in related fields. Then, many attack detection …

Text classification is one of the fundamental tasks in natural language processing, which
requires an agent to determine the most appropriate category for input sentences. Recently …

Emotion recognition in conversation (ERC) based on multiple modalities has attracted
enormous attention. However, most research simply concatenated multimodal …

In partial multi-label learning (PML), each data example is equipped with a candidate label
set, which consists of multiple ground-truth labels and other false-positive labels. Recently …

Social media has evolved into a platform for the dissemination of information, including fake
news. There is a lot of false information about the current situation of the Coronavirus …