Twitter Sentiment Analysis using Python Program

Twitter sentiment analysis allows us to analyze public opinions and emotions from tweets using Python. We'll use the Twitter API to fetch tweets and TextBlob library to analyze their sentiment polarity.

Prerequisites

Before starting, you need ?

• A Twitter account with verified phone number
• Twitter Developer Account for API access
• Python installed on your system

Setting Up Twitter API

Visit the Twitter Developer portal and create a new app. After creating the app, navigate to the "Keys and Tokens" tab to obtain your API credentials ?

# Twitter API credentials
consumer_key = 'your_consumer_key'
consumer_secret = 'your_consumer_secret'
access_token = 'your_access_token'
access_token_secret = 'your_access_token_secret'

Installing Dependencies

Install the required Python libraries ?

pip install tweepy textblob

What is TextBlob?

TextBlob is a Python library for processing textual data. It provides a simple API for diving into common natural language processing tasks such as sentiment analysis. The sentiment polarity ranges from -1 (negative) to +1 (positive).

Complete Twitter Sentiment Analysis Script

import tweepy
from textblob import TextBlob

# Twitter API credentials
consumer_key = 'your_consumer_key'
consumer_secret = 'your_consumer_secret'
access_token = 'your_access_token'
access_token_secret = 'your_access_token_secret'

# Authentication
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)

# Create API object
api = tweepy.API(auth)

# Search for tweets
search_term = 'Python programming'
public_tweets = api.search_tweets(q=search_term, count=10)

print(f"Analyzing sentiment for tweets about: {search_term}\n")

for tweet in public_tweets:
    print(f"Tweet: {tweet.text}")
    
    # Perform sentiment analysis
    analysis = TextBlob(tweet.text)
    
    print(f"Polarity: {analysis.sentiment.polarity:.2f}")
    print(f"Subjectivity: {analysis.sentiment.subjectivity:.2f}")
    
    # Classify sentiment
    if analysis.sentiment.polarity > 0:
        sentiment = "Positive"
    elif analysis.sentiment.polarity < 0:
        sentiment = "Negative"
    else:
        sentiment = "Neutral"
    
    print(f"Sentiment: {sentiment}")
    print("-" * 50)

Understanding Sentiment Metrics

Example Output

Analyzing sentiment for tweets about: Python programming

Tweet: Python is an amazing programming language! Love it.
Polarity: 0.75
Subjectivity: 0.90
Sentiment: Positive
--------------------------------------------------
Tweet: Python programming can be challenging sometimes.
Polarity: -0.25
Subjectivity: 0.50
Sentiment: Negative
--------------------------------------------------

Enhanced Analysis Function

def analyze_sentiment(text):
    """
    Analyze sentiment of given text
    Returns sentiment classification and scores
    """
    analysis = TextBlob(text)
    polarity = analysis.sentiment.polarity
    subjectivity = analysis.sentiment.subjectivity
    
    if polarity > 0.1:
        sentiment = "Positive"
    elif polarity < -0.1:
        sentiment = "Negative"
    else:
        sentiment = "Neutral"
    
    return {
        'sentiment': sentiment,
        'polarity': polarity,
        'subjectivity': subjectivity
    }

# Usage example
tweet_text = "I love learning Python programming!"
result = analyze_sentiment(tweet_text)
print(f"Sentiment: {result['sentiment']}")
print(f"Polarity: {result['polarity']:.2f}")

Conclusion

Twitter sentiment analysis using Python helps extract valuable insights from social media data. By combining Tweepy for data collection and TextBlob for sentiment analysis, you can build powerful tools to understand public opinion and emotions expressed in tweets.