A Deep Language-independent Network to analyze the impact of COVID-19 on the World via Sentiment Analysis

TL;DR

This paper introduces a novel deep language-independent neural network, MACBiG-Net, for analyzing COVID-19 related sentiments from Twitter data across five severely affected countries, providing insights into public opinion during the pandemic.

Contribution

The paper presents a new multilevel attention-based Conv-BiGRU network architecture for sentiment analysis that is language-independent and tailored for COVID-19 Twitter data.

Findings

The proposed MACBiG-Net effectively captures sentiment in COVID-19 tweets.

The model outperforms baseline methods on the COVID-19 sentiment dataset.

Visualization of attention weights provides interpretability of sentiment analysis results.

Abstract

Towards the end of 2019, Wuhan experienced an outbreak of novel coronavirus, which soon spread all over the world, resulting in a deadly pandemic that infected millions of people around the globe. The government and public health agencies followed many strategies to counter the fatal virus. However, the virus severely affected the social and economic lives of the people. In this paper, we extract and study the opinion of people from the top five worst affected countries by the virus, namely USA, Brazil, India, Russia, and South Africa. We propose a deep language-independent Multilevel Attention-based Conv-BiGRU network (MACBiG-Net), which includes embedding layer, word-level encoded attention, and sentence-level encoded attention mechanism to extract the positive, negative, and neutral sentiments. The embedding layer encodes the sentence sequence into a real-valued vector. The word-level and sentence-level encoding is performed by a 1D Conv-BiGRU based mechanism, followed by word-level and sentence-level attention, respectively. We further develop a COVID-19 Sentiment Dataset by crawling the tweets from Twitter. Extensive experiments on our proposed dataset demonstrate the effectiveness of the proposed MACBiG-Net. Also, attention-weights visualization and in-depth results analysis shows that the proposed network has effectively captured the sentiments of the people.