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Unbalanced Sentiment Analysis Classes

vikash11
Posts: 15
Hello,
I’m presently working on a Sentiment Analysis project that uses a movie reviews dataset similar to this one, and I’m having trouble with class imbalance. The dataset comprises both good and negative evaluations, however the positive ratings outnumber the negative ones by a large margin. Here’s an example of my code:
import pandas as pd from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import CountVectorizer from sklearn.naive_bayes import MultinomialNB # Load the movie reviews dataset data = pd.read_csv('movie_reviews.csv') # Preprocess the data # ... (code for data preprocessing) # Split the dataset into training and testing sets X_train, X_test, y_train, y_test = train_test_split(data['review'], data['sentiment'], test_size=0.2, random_state=42) # Vectorize the text data using CountVectorizer vectorizer = CountVectorizer() X_train_vectorized = vectorizer.fit_transform(X_train) X_test_vectorized = vectorizer.transform(X_test) # Train the Naive Bayes classifier model = MultinomialNB() model.fit(X_train_vectorized, y_train) # Evaluate the model accuracy = model.score(X_test_vectorized, y_test) print(f"Accuracy: {accuracy}")
The code’s accuracy is approximately 90%, however I assume it is biassed towards the majority class (positive reviews) owing to the class imbalance issue. I want to make sure that my model responds well to both positive and negative feedback. How can I handle the issue of class imbalance while also improving the overall effectiveness of my sentiment analysis model?
Thank you for your assistance!
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