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# Sentiment Analysis of English Tweets (including COVID-19-specific 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,    
      &nbsp;&nbsp;title={Twitter conversations predict the daily confirmed COVID-19 cases},    
      &nbsp;&nbsp;author={Lamsal, Rabindra and Harwood, Aaron and Read, Maria Rodriguez},    
      &nbsp;&nbsp;journal={arXiv preprint arXiv:2206.10471},    
      &nbsp;&nbsp;year={2022}        
}


## 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/finetuned-bertweet-sentiment-analysis")
    
    model = TFAutoModelForSequenceClassification.from_pretrained("rabindralamsal/finetuned-bertweet-sentiment-analysis")

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