✅ Python Project Sentiment Analysis with Machine Learning

 

✅ Python Project Sentiment Analysis with Machine Learning 🧠📊

📌 Problem Statement:
Build a model that classifies text into Positive, Negative, or Neutral sentiment — useful for understanding opinions in social media, reviews, or feedback.

🧠 What You’ll Learn

  • Cleaning and preprocessing text data

  • Converting text to numerical vectors using TF-IDF

  • Training a Logistic Regression classifier

  • Evaluating performance with metrics

  • Predicting sentiment for custom text inputs

✅ Python Code Example

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

# 1. Load dataset
data = pd.read_csv("sentiment.csv")  # Columns: 'text' and 'sentiment'

# 2. Optional: Encode sentiment labels if needed (Positive=1, Negative=0, Neutral=2)

# 3. Split data
X_train, X_test, y_train, y_test = train_test_split(
    data['text'], data['sentiment'], test_size=0.2, random_state=42
)

# 4. Vectorize text using TF-IDF
tfidf = TfidfVectorizer(stop_words='english')
X_train_vec = tfidf.fit_transform(X_train)
X_test_vec = tfidf.transform(X_test)

# 5. Train Logistic Regression model
sentiment_model = LogisticRegression(max_iter=1000)
sentiment_model.fit(X_train_vec, y_train)

# 6. Evaluate model
y_pred = sentiment_model.predict(X_test_vec)
print(classification_report(y_test, y_pred))

🛠️ Requirements

pip install pandas scikit-learn

🔧 Try Enhancing

  • Use SVM or Random Forest for better performance

  • Integrate NLTK or spaCy for advanced text preprocessing

  • Build a Streamlit app for live sentiment predictions

💡 Real-World Use Cases

✅ Monitor social media sentiment
✅ Analyze customer feedback
✅ Extract insights from product reviews
✅ Strong NLP project for portfolios

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