Twitter Sentiment Analysis

This project performs sentiment analysis on tweets using the TextBlob library. It classifies tweets into positive, negative, or neutral categories and visualizes results using word clouds and plots.

📂 Dataset

• tweets.csv from Kaggle (pre-cleaned with sentiment and text columns)
• Kaggle Link: "https://www.kaggle.com/datasets/kazanova/sentiment140"

🛠️ Tools & Libraries

• Python
• Pandas
• TextBlob
• Matplotlib
• Seaborn
• WordCloud

📈 Project Workflow

1. Load and inspect data
2. Preprocess text (remove links, mentions, special chars)
3. Analyze sentiment polarity using TextBlob
4. Label sentiment as positive, neutral, or negative
5. Visualize results using Word Clouds and Count Plots

🖼️ Visualizations

• Word clouds for positive & negative tweets
• Sentiment distribution bar chart

📌 Output

Insight into public sentiment based on tweets using natural language processing (NLP).

---