Ex-Twit: Explainable Twitter Mining on Health Data

This research question is one of the main motivations of our work to explain the prediction of model. Since most machine learning models provide no Twitter has been growing in popularity and now-a-days, it explanations for the predictions, their predictions is used everyday by people to express opinions about different are obscure for the human. The ability to explain topics, such as products, movies, health, music, politicians, a model's prediction has become a necessity events, among others. Twitter data constitutes a rich in many applications including Twitter mining. In source that can be used for capturing information about any this work, we propose a method called Explainable topic imaginable. This data can be used in different use cases Twitter Mining (Ex-Twit) combining Topic Modeling such as finding trends related to a specific keyword, measuring and Local Interpretable Model-agnostic Explanation brand sentiment, and gathering feedback about new products (LIME) to predict the topic and explain the and services. In this work, we use text mining to mine the model predictions. We demonstrate the effectiveness Twitter health-related data. Text mining is the application of of Ex-Twit on Twitter health-related data.