Large models for time series and spatio-temporal data: A survey and outlook
Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …
applications. They capture dynamic system measurements and are produced in vast …
Sentiment analysis and emotion understanding during the COVID-19 pandemic in Spain and its impact on digital ecosystems
C de Las Heras-Pedrosa, P Sánchez-Núñez… - International journal of …, 2020 - mdpi.com
COVID-19 has changed our lives forever. The world we knew until now has been
transformed and nowadays we live in a completely new scenario in a perpetual restructuring …
transformed and nowadays we live in a completely new scenario in a perpetual restructuring …
Harnessing artificial intelligence to combat online hate: Exploring the challenges and opportunities of large language models in hate speech detection
Large language models (LLMs) excel in many diverse applications beyond language
generation, eg, translation, summarization, and sentiment analysis. One intriguing …
generation, eg, translation, summarization, and sentiment analysis. One intriguing …
Mining dual emotion for fake news detection
Emotion plays an important role in detecting fake news online. When leveraging emotional
signals, the existing methods focus on exploiting the emotions of news contents that …
signals, the existing methods focus on exploiting the emotions of news contents that …
A deep learning-based approach for multi-label emotion classification in tweets
Currently, people use online social media such as Twitter or Facebook to share their
emotions and thoughts. Detecting and analyzing the emotions expressed in social media …
emotions and thoughts. Detecting and analyzing the emotions expressed in social media …
Relation extraction from clinical narratives using pre-trained language models
Natural language processing (NLP) is useful for extracting information from clinical
narratives, and both traditional machine learning methods and more-recent deep learning …
narratives, and both traditional machine learning methods and more-recent deep learning …
Emotion detection for social robots based on NLP transformers and an emotion ontology
For social robots, knowledge regarding human emotional states is an essential part of
adapting their behavior or associating emotions to other entities. Robots gather the …
adapting their behavior or associating emotions to other entities. Robots gather the …
A closer look at classification evaluation metrics and a critical reflection of common evaluation practice
J Opitz - Transactions of the Association for Computational …, 2024 - direct.mit.edu
Classification systems are evaluated in a countless number of papers. However, we find that
evaluation practice is often nebulous. Frequently, metrics are selected without arguments …
evaluation practice is often nebulous. Frequently, metrics are selected without arguments …
Practical galaxy morphology tools from deep supervised representation learning
M Walmsley, AMM Scaife, C Lintott… - Monthly Notices of …, 2022 - academic.oup.com
Astronomers have typically set out to solve supervised machine learning problems by
creating their own representations from scratch. We show that deep learning models trained …
creating their own representations from scratch. We show that deep learning models trained …
Transformer-based label set generation for multi-modal multi-label emotion detection
Multi-modal utterance-level emotion detection has been a hot research topic in both multi-
modal analysis and natural language processing communities. Different from traditional …
modal analysis and natural language processing communities. Different from traditional …