Identifying informative tweets during a pandemic via a topic-aware neural language model

Abstract

Every epidemic affects the real lives of many people around the world and leads to terrible consequences. Recently, many tweets about the COVID-19 pandemic have been shared publicly on social media platforms. The analysis of these tweets is helpful for emergency response organizations to prioritize their tasks and make better decisions. However, most of these tweets are non-informative, which is a challenge for establishing an automated system to detect useful information in social media. Furthermore, existing methods ignore unlabeled data and topic background knowledge, which can provide additional semantic information. In this paper, we propose a novel Topic-Aware BERT (TABERT) model to solve the above challenges. TABERT first leverages a topic model to extract the latent topics of tweets. Secondly, a flexible framework is used to combine topic information with the output of BERT. Finally, we adopt adversarial training to achieve semi-supervised learning, and a large amount of unlabeled data can be used to improve inner representations of the model. Experimental results on the dataset of COVID-19 English tweets show that our model outperforms classic and state-of-the-art baselines.

Sentiment Analysis of COVID-19 on Weibo text using optimized Bi-LSTM model

“…Hussain et al [33] conducted sentiment analysis of data on social mediafrom March 1 to November 22, 2020 in the United States and the United Kingdom to compare the average sentiment, sentiment trends, and discussion topics of US and UK netizens regarding the outbreak and discuss their relevance. Gao et al [34] proposed Topic-Aware BERT (TABERT) model for establishing an automated system to detect useful information in social media.…”