Work

Sarcasm Detection in News Headlines Using Bidirectional LSTM

Year: 2025

Type: article

Abstract: This project explores sarcasm detection in news headlines using a Bidirectional LSTM model. It involves preprocessing text data, embedding words with TensorFlow, and training a deep learning model to classify whether a headline is sarcastic or not. The notebook includes data cleaning, model architecture, training process, and evaluation metrics.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17344851

Author: Pizzetti, Mattia

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7090483478

FWCI:

Cites:

Cited by:

Related to: