| [14] |
Machine learning (Naïve Bayes) |
Covid-19 tweets sentiment classification |
Over 900000 Covid-19 tweets from February to March 2020 |
| [22] |
Deep learning (LSTM RNN) |
Covid-19 reddit post sentiment classification & topic finding |
Total 563,079 Covid-19 related comment from reddit |
| [25] |
Deep learning classifier (fuzzy rule based model) |
Covid-19 tweets sentiment classification |
Two dataset: DATA_SET 1 226,668 tweets & DATA_SET 2 most re-tweeted tweets (23000) |
| [28] |
Deep learning (ERNIE & BiLSTM + attention + CRF) |
Covid-19 blog post sentiment classification for Chinese Text |
60000 micro-blog Chinese text post related COVID-19 |
| [29] |
Lexicon-based technique (
Word-Emotion Lexicon) |
Covid-19 news headline sentiments and emotions classification |
Total 141,208 news headlines of global English news sources |
| [23] |
Deep learning (Deep LSTM) |
Covid-19 tweets cross-cultural polarity and emotion detection |
Total 460,286 Covid-19 related tweets |
| [24] |
Natural Language Processing Techniques |
Topical sentiment analysis for Covid-19 tweets |
Over 1 million Covid-19 mask-related tweets |
| [26] |
Machine learning (BERT model with TF-IDF features) |
Topic discovering behind Covid-19 negative tweets |
Total 999978 randomly selected COVID-19 related Weibo posts |
