What is Sentiment Analysis?

Sentiment analysis, also known as opinion mining, is the process of determining the emotional tone behind words to gain an understanding of the attitudes, opinions, and emotions of a speaker or writer.

Key Applications

  • Social Media Monitoring: Track public opinion about brands or products
  • Customer Feedback Analysis: Automatically categorize reviews as positive/negative
  • Market Research: Identify trends in user-generated content
  • Emotion Recognition: Detect happiness, anger, or sadness in text

Getting Started with NLTK

  1. Install NLTK 

    pip install nltk

    📌 Check our Python environment guide for installation tips

  2. Basic Setup 

    import nltk  
from nltk.sentiment import SentimentIntensityAnalyzer  

nltk.download('vader_lexicon')  
sia = SentimentIntensityAnalyzer()  
score = sia.polarity_scores("I love this product!")  
print(score)

  3. Interpreting Results

    • pos: Positive sentiment score
    • neg: Negative sentiment score
    • neu: Neutral sentiment score
    • compound: Normalized score (-1 to 1)

Visualizing Sentiment Data

📊 Example visualization using matplotlib: 

import matplotlib.pyplot as plt  

# Sample data  
sentiments = [0.5, -0.2, 0.8, -0.3, 0.1]  

plt.plot(sentiments, marker='o')  
plt.title("Sentiment Trends")  
plt.xlabel("Text Samples")  
plt.ylabel("Polarity Score")  
plt.grid(True)  
plt.show()

Advanced Techniques

  • Custom Lexicon Training: Build domain-specific sentiment dictionaries
  • Text Preprocessing: Tokenization, stopword removal, and stemming
  • Machine Learning Models: Train classifiers with Naive Bayes or SVM

Expand Your Knowledge

📖 Explore our NLP fundamentals tutorial to deepen your understanding of text analysis techniques.

Sentiment_Analysis
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