Extracting Information from Microblogs Posted During Natural Disasters

Abstract

Social Networking websites plays an important role in our life. The usage of the above websites is in every domain of our lives and they have increasingly infused itself into daily life. In recent years, the social networking websites such as Twitter, Facebook, are used in natural disasters. Many features have been included in Twitter for fast responses in such kind of unexpected events. This paper is based on the experiments performed on the 2017 Microblog Track provided by Forum of Information Retrieval & Evaluation. The Classification schemes are used with two predefined labels as need and availability. The various pre-processing and natural language processing techniques are applied before the training of the model. The experiments showed that the classification accuracy is increased when the term weight is modified by using the information gain method and using the SVM classifier. This system automatically annotated the FIRE-2015 dataset of microblog track with 97% accuracy.