Title: Enhanced classification of crisis related tweets using deep learning models and word embeddings
Authors: Dharini Ramachandran; R. Parvathi
Addresses: School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India ' School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
Abstract: Social media plays a crucial role during emergency events by preserving intelligence about the current condition, which may save lives. Twitter is one such powerful social media platform where information about the situational awareness is directly posted by victims or bystanders. The objective of the research is to enhance the classification of crisis related tweets by utilising the deep learning models. Our work focuses on evaluating the deep learning models, the vectorisation methods and the effect of data size on them. A multilayer perceptron (MLP), a convolutional neural network (CNN) and a long short term memory (LSTM) are employed along with the vectorisation methods (GloVe and Word2Vec), in different experiments. Based on the results pertaining to the metrics of classification and the learning graphs, the LSTM model is observed to work well. The need for measures, to improve the classification of a large twitter dataset is understood from the analysis.
Keywords: deep learning; Twitter analytics; long short-term memory; LSTM; convolutional neural network; CNN; GloVe and Word2Vec embeddings; social media text analytics; crisis analytics.
International Journal of Web Engineering and Technology, 2021 Vol.16 No.2, pp.158 - 186
Received: 09 Nov 2020
Accepted: 03 May 2021
Published online: 23 Sep 2021 *