Build a Sentiment Analyzer with Python

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Build a Sentiment Analyzer with Python | DarchumsTech

Build a Sentiment Analyzer with Python

By DarchumsTech

Introduction

Sentiment analysis is a powerful technique in Natural Language Processing (NLP) that determines the emotional tone behind textual data. In this tutorial, we'll walk through building a sentiment analyzer using Python, leveraging libraries like NLTK and scikit-learn.

Prerequisites

  • Basic understanding of Python programming
  • Familiarity with machine learning concepts
  • Python installed on your system

Step 1: Import Libraries

import pandas as pd
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer
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

Step 2: Load and Preprocess Data

# Load dataset
df = pd.read_csv('reviews.csv')  # Ensure you have a CSV file with 'review' and 'sentiment' columns

# Preprocess text
df['cleaned_review'] = df['review'].str.lower().str.replace('[^\w\s]', '')

Step 3: Feature Extraction

# Vectorize text data
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(df['cleaned_review'])
y = df['sentiment']

Step 4: Train the Model

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train classifier
model = LogisticRegression()
model.fit(X_train, y_train)

Step 5: Evaluate the Model

# Predict and evaluate
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))

Conclusion

You've successfully built a sentiment analyzer using Python! This tool can be expanded further by incorporating more sophisticated models or deploying it as a web application.

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