Sentiment Analysis of English Tweets with BERTsent

BERTsent: A finetuned BERT based sentiment classifier for English language tweets.

BERTsent is trained with SemEval 2017 corpus (39k plus tweets) and is based on bertweet-base that was trained on 850M English Tweets (cased) and additional 23M COVID-19 English Tweets (cased). The base model used RoBERTa pre-training procedure.

Output labels:

0 represents "negative" sentiment
1 represents "neutral" sentiment
2 represents "positive" sentiment

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:

[[0.9862386  0.01050556 0.00325586]]
0