| # Sentiment Analysis of English Tweets with BERTsent | |
| **BERTsent**: A finetuned **BERT** based **sent**iment classifier for English language tweets. | |
| BERTsent is trained with SemEval 2017 corpus (39k plus tweets) and is based on [bertweet-base](https://github.com/VinAIResearch/BERTweet) that was trained on 850M English Tweets (cased) and additional 23M COVID-19 English Tweets (cased). The base model used [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) pre-training procedure. | |
| Output labels: | |
| - 0 represents "negative" sentiment | |
| - 1 represents "neutral" sentiment | |
| - 2 represents "positive" sentiment | |
| ## COVID-19 tweets specific task | |
| Eg., | |
| The model distinguishes: "covid" -> neutral sentiment, "I have covid" -> negative sentiment | |
| ## Cite | |
| If you use BERTsent in your project/research, please cite the following article: | |
| Lamsal, R., Harwood, A., & Read, M. R. (2022). [Twitter conversations predict the daily confirmed COVID-19 cases](https://arxiv.org/abs/2206.10471). arXiv preprint arXiv:2206.10471. | |
| @article{lamsal2022twitter, | |
| title={Twitter conversations predict the daily confirmed COVID-19 cases}, | |
| author={Lamsal, Rabindra and Harwood, Aaron and Read, Maria Rodriguez}, | |
| journal={Applied Soft Computing}, | |
| volume={129}, | |
| pages={109603}, | |
| year={2022}, | |
| publisher={Elsevier} | |
| } | |
| ## Using the model | |
| Install transformers and emoji, if already not installed: | |
| terminal: | |
| pip install transformers | |
| pip install emoji (for converting emoticons or emojis into text) | |
| notebooks (Colab, Kaggle): | |
| !pip install transformers | |
| !pip install emoji | |
| Import BERTsent from the transformers library: | |
| from transformers import AutoTokenizer, TFAutoModelForSequenceClassification | |
| tokenizer = AutoTokenizer.from_pretrained("rabindralamsal/BERTsent") | |
| model = TFAutoModelForSequenceClassification.from_pretrained("rabindralamsal/BERTsent") | |
| Import TensorFlow and numpy: | |
| import tensorflow as tf | |
| import numpy as np | |
| We have installed and imported everything that's needed for the sentiment analysis. Let's predict sentiment of an example tweet: | |
| example_tweet = "The NEET exams show our Govt in a poor light: unresponsiveness to genuine concerns; admit cards not delivered to aspirants in time; failure to provide centres in towns they reside, thus requiring unnecessary & risky travels. What a disgrace to treat our #Covid warriors like this!" | |
| #this tweet resides on Twitter with an identifier-1435793872588738560 | |
| input = tokenizer.encode(example_tweet, return_tensors="tf") | |
| output = model.predict(input)[0] | |
| prediction = tf.nn.softmax(output, axis=1).numpy() | |
| sentiment = np.argmax(prediction) | |
| print(prediction) | |
| print(sentiment) | |
| Output: | |
| [[0.972672164440155 0.023684727028012276 0.003643065458163619]] | |
| 0 |