COVID-19 Tweet Analysis using Hybrid Keyword Extraction Approach

Aim of this paper is to identify the key issues discussed among the people on Twitter using machine learning and NLP techniques regarding COVID-19. One of the most important way to produce these insights is Automatic Keyword Extraction. It is a method to obtain the most important words from the text by summarizing thus providing an insight into the whole context. Text Summarization is the process to condense the content without the loss of significant data. This paper applies a hybrid model of graph-based and topic modeling approaches to extract keywords from a large dataset of approximately 1 million tweets. © 2021 IEEE.

Masked Face Analysis via Multi-Task Deep Learning.

Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to yield predictions of age, expression, and gender for the masked face. Through extensive experiments, the proposed framework has been found to provide a better performance than other existing methods.