most citedMethods for Generating Drift in Text Streams

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV2024

Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models

Israel A. Laurensi, Alceu de Souza Britto, Jean Paul Barddal +1

Facial expression recognition is a pivotal component in machine learning, facilitating various applications. However, convolutional neural networks (CNNs) are often plagued by cata…

cs.LG2024

Dynamic Modality and View Selection for Multimodal Emotion Recognition with Missing Modalities

Luciana Trinkaus Menon, Luiz Carlos Ribeiro Neduziak, Jean Paul Barddal +2

The study of human emotions, traditionally a cornerstone in fields like psychology and neuroscience, has been profoundly impacted by the advent of artificial intelligence (AI). Mul…

cs.LG20241 cited

Methods for Generating Drift in Text Streams

Cristiano Mesquita Garcia, Alessandro Lameiras Koerich, Alceu de Souza Britto +1

Systems and individuals produce data continuously. On the Internet, people share their knowledge, sentiments, and opinions, provide reviews about services and products, and so on.…

cs.CL2024

Improving Sampling Methods for Fine-tuning SentenceBERT in Text Streams

Cristiano Mesquita Garcia, Alessandro Lameiras Koerich, Alceu de Souza Britto +1

The proliferation of textual data on the Internet presents a unique opportunity for institutions and companies to monitor public opinion about their services and products. Given th…

cs.SI2024

Temporal Analysis of Drifting Hashtags in Textual Data Streams: A Graph-Based Application

Cristiano M. Garcia, Alceu de Souza Britto, Jean Paul Barddal

Initially supported by Twitter, hashtags are now used on several social media platforms. Hashtags are helpful for tagging, tracking, and grouping posts on similar topics. In this p…

cs.LG2023

Concept Drift Adaptation in Text Stream Mining Settings: A Systematic Review

Cristiano Mesquita Garcia, Ramon Simoes Abilio, Alessandro Lameiras Koerich +2

The society produces textual data online in several ways, e.g., via reviews and social media posts. Therefore, numerous researchers have been working on discovering patterns in tex…