using the transformers library for sentiment analysis

transformers-sentiment-analysis.py

from transformers import pipeline
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import pandas as pd

df = pd.read_csv('amazon_cells_labelled.txt', delimiter='\t', header=None, names=['Review', 'Sentiment'])

classifier = pipeline("sentiment-analysis")

predicted_sentiments = []

for index, row in df.iterrows():
  result = classifier(row['Review'])
  if result[0]['label'] == 'POSITIVE':
    sentiment = 1
  else:
    sentiment = 0
  predicted_sentiments.append(sentiment)

df['predicted_sentiment'] = predicted_sentiments

# Evaluate the performance using accuracy, confusion matrix, and classification report
accuracy = accuracy_score(df['Sentiment'], df['predicted_sentiment'])
conf_matrix = confusion_matrix(df['Sentiment'], df['predicted_sentiment'])
class_report = classification_report(df['Sentiment'], df['predicted_sentiment'])

accuracy, conf_matrix, class_report