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Embedding-based Approaches to Hyperpartisan News Detection

2025-01-02 · Karthik Mohan, PengYu Chen

In this paper, we describe our systems in which the objective is to determine whether a given news article could be considered as hyperpartisan. Hyperpartisan news is news that takes an extremely polarized political standpoint with an intention of creating political divide among the public. We attempted several approaches, including n-grams, sentiment analysis, as well as sentence and document representation using pre-tained ELMo. Our best system using pre-trained ELMo with Bidirectional LSTM achieved an accuracy of 83% through 10-fold cross-validation without much hyperparameter tuning.

📄 PDF Abstract BibTeX arXiv:2501.01370

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SentenceSentiment Analysis

Methods 이 논문이 사용한 방법론

Tanh Activation 설명 없음
Sigmoid Activation 설명 없음
Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
BiLSTM A Bidirectional LSTM, or biLSTM, is a sequence processing model that consists of two LSTMs: one taking the input in a forward direction, and the other in a backwards…
ELMo Embeddings from Language Models, or ELMo, is a type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and…
LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…

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