How to handle imbalanced data in text classification?

This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets. May 12, 2021 ... Adapting or fine-tuning a language model to the radiology “language” can greatly improve the results of text classification models. Introduction. In this article, I will explain about the text classification and the step by step process to implement it in python. Jul 17, 2019 ... In this work, we propose to use graph convolutional networks for text classification. We build a single text graph for a corpus based on word co ... ... text classification to improve the detection of the target classes from Twitter. We reviewed the literature related to short text classification of ... Feb 28, 2024 ... Text Classification. Text classification analyses the text and identifies categories for the content with a confidence score. Text ... Aug 1, 2021 ... The general format of natural language inference (NLI) makes it tempting to be used for zero-shot text classification by casting any target ... Oct 28, 2013 ... Text categorization ... Text categorization (a.k.a. text classification) is the task of assigning predefined categories to free-text documents. It ... Our experiments have shown that both approaches can interact with LLMs successfully to classify text—if certain criteria are met ... The documents to be classified may be texts, images, music, etc. Each kind of document possesses its special classification problems. When not otherwise ...