Identification and Classification of Informative Tweets During Disasters

Abstract

bstract: Twitter serves as a popular social media platform for sharing information during disasters. However, the task of distinguishing informative tweets from the overwhelming volume of content can be daunting. In this research, we propose an innovative approach that combines Natural Language Processing (NLP), Exploratory Data Analysis (EDA), Support Vector Machine (SVM), and the Xception algorithm. Our objective is to effectively categorize disaster-related tweets based on their textual and visual content. By leveraging TensorFlow and Keras, we aim to accurately classify tweets as either disaster-related or unrelated. Additionally, we integrate the Google Translate API to facilitate the translation of text data into multiple languages. To enhance the precision of tweet label prediction, we utilize the late fusion technique to consolidate the output of our text-based model.