# Importing necessary libraries import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score
# Sample dataset (replace with your dataset) data = pd.DataFrame({ 'text': ["I love this movie!", "This movie is terrible.", "Neutral tweet about something."], 'sentiment': ['positive', 'negative', 'neutral'] })
# Split data into features (X) and target (y) X = data['text'] y = data['sentiment']
# Vectorize text data using TF-IDF representation vectorizer = TfidfVectorizer() X_vect = vectorizer.fit_transform(X)
# Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X_vect, y, test_size=0.2, random_state=42)
# Build and train Logistic Regression model model = LogisticRegression() model.fit(X_train, y_train)
# Evaluate model y_pred = model.predict(X_test) accuracy = accuracy_score(y_test, y_pred)