2019/05/24 by Tunazzina Islam, Islam, Tunazzina · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Topic Modeling #cs.AI #cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.1906.02132
In SocialNLP 2019 @ IJCAI-2019
openalex publication_date 2019/05/24 · arxiv created 2019/06/22 · arxiv updated 2020/12/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Since most machine learning models provide no explanations for the predictions, their predictions are obscure for the human. The ability to explain a model's prediction has become a necessity in many applications including Twitter mining. In this work, we propose a method called Explainable Twitter Mining (Ex-Twit) combining Topic Modeling and Local Interpretable Model-agnostic Explanation (LIME) to predict the topic and explain the model predictions. We demonstrate the effectiveness of Ex-Twit on Twitter health-related data.