# BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets arXiv: https://arxiv.org/abs/2211.09733 ยท HTML: https://mlsanity.com/paper/2211.09733/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 91 references that are in the map. - First posted on arXiv 4 Nov 2022. ## Builds on (most cited first, 28 of 28) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019) - [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019) - [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019) - [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019) - [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019) - [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020) - [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019) - [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020) - [SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization](https://mlsanity.com/paper/1911.03437/) (2019) - [COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter](https://mlsanity.com/paper/2005.07503/) (2020) - [Data augmentation instead of explicit regularization](https://mlsanity.com/paper/1806.03852/) (2018) - [Fine-grained Sentiment Classification using BERT](https://mlsanity.com/paper/1910.03474/) (2019) - [Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach](https://mlsanity.com/paper/2004.11695/) (2020) - [COVID-19 sentiment analysis via deep learning during the rise of novel cases](https://mlsanity.com/paper/2104.10662/) (2021) - [Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference](https://mlsanity.com/paper/2002.04815/) (2020) - [g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detection](https://mlsanity.com/paper/2012.11967/) (2020) - [Cross-language sentiment analysis of European Twitter messages duringthe COVID-19 pandemic](https://mlsanity.com/paper/2008.12172/) (2020) - [Examination of Community Sentiment Dynamics due to COVID-19 Pandemic: A Case Study from A State in Australia](https://mlsanity.com/paper/2006.12185/) (2020) - [Large-scale, Language-agnostic Discourse Classification of Tweets During COVID-19](https://mlsanity.com/paper/2008.00461/) (2020) - [An Exploratory Study of COVID-19 Information on Twitter in the Greater Region](https://mlsanity.com/paper/2008.05900/) (2020) - [Fighting the COVID-19 Infodemic with a Holistic BERT Ensemble](https://mlsanity.com/paper/2104.05745/) (2021) - [Multi-channel CNN to classify nepali covid-19 related tweets using hybrid features](https://mlsanity.com/paper/2203.10286/) (2022) - [Monitoring Depression Trend on Twitter during the COVID-19 Pandemic](https://mlsanity.com/paper/2007.00228/) (2020) - [CIA_NITT at WNUT-2020 Task 2: Classification of COVID-19 Tweets Using Pre-trained Language Models](https://mlsanity.com/paper/2009.05782/) (2020) - [Suggestion Mining from Online Reviews using ULMFiT](https://mlsanity.com/paper/1904.09076/) (2019) - [Text Classification of Manifestos and COVID-19 Press Briefings using BERT and Convolutional Neural Networks](https://mlsanity.com/paper/2010.10267/) (2020) ## Built on by (most cited first, 2 of 2) - [CrosGrpsABS: Cross-Attention over Syntactic and Semantic Graphs for Aspect-Based Sentiment Analysis in a Low-Resource Language](https://mlsanity.com/paper/2505.19018/) (2025) - [HausaMovieReview: A Benchmark Dataset for Sentiment Analysis in Low-Resource African Language](https://mlsanity.com/paper/2509.16256/) (2025) ## Abstract (arXiv) The free flow of information has been accelerated by the rapid development of social media technology. There has been a significant social and psychological impact on the population due to the outbreak of Coronavirus disease (COVID-19). The COVID-19 pandemic is one of the current events being discussed on social media platforms. In order to safeguard societies from this pandemic, studying people's emotions on social media is crucial. As a result of their particular characteristics, sentiment analysis of texts like tweets remains challenging. Sentiment analysis is a powerful text analysis tool. It automatically detects and analyzes opinions and emotions from unstructured data. Texts from a wide range of sources are examined by a sentiment analysis tool, which extracts meaning from them, including emails, surveys, reviews, social media posts, and web articles. To evaluate sentiments, natural language processing (NLP) and machine learning techniques are used, which assign weights to entities, topics, themes, and categories in sentences or phrases. Machine learning tools learn how to detect sentiment without human intervention by examining examples of emotions in text. In a pandemic situation, analyzing social media texts to uncover sentimental trends can be very helpful in gaining a better understanding of society's needs and predicting future trends. We intend to study society's perception of the COVID-19 pandemic through social media using state-of-the-art BERT and Deep CNN models. The superiority of BERT models over other deep models in sentiment analysis is evident and can be concluded from the comparison of the various research studies mentioned in this article.