gl-ankit-kumar / prodigy_ds_04

PRODIGY_DS_04

EXPLORATORY DATA ANALYSIS OVER TITANIC DATASET

Prodigy Infotech Data Science Internship Task 04

Twitter Sentiment Analysis 📊🐦

Embark on an exciting journey into the world of Twitter data and sentiment analysis! This project involves Python, Jupyter Notebook, and essential libraries such as Matplotlib, Pandas, Numpy, and Scikit-learn.

Tools Utilized

• Python
• Jupyter Notebook
• Matplotlib Library
• Pandas
• Numpy
• Scikit-learn (for sentiment analysis)

Overview of the Task

The objective of this task is to perform an in-depth analysis of Twitter data, including exploratory data analysis (EDA) and sentiment analysis. Dive into Python coding and utilize powerful libraries to extract valuable insights from the Twitter dataset.

Task Requirements

1. Import Libraries

1.1 Import the necessary libraries: Matplotlib, Pandas, Numpy, and Scikit-learn.

2. Load the Dataset

2.1 Utilize the Twitter dataset for analysis.

3. Data Exploration

3.1 Uncover patterns and trends in the Twitter dataset. 3.2 Create visualizations to represent key insights.

4. Sentiment Analysis

4.1 Apply sentiment analysis to understand the emotional tone of tweets. 4.2 Visualize sentiment distribution and patterns.

5. Conclusion

This data science project provides a comprehensive analysis of Twitter data, spanning exploratory data analysis and sentiment analysis. Dive into visualizations, uncover trends, and gain insights into the sentiment of tweets.

🛠️ Setup and Usage

1. Clone the repository:

   git clone https://github.com/gl-ankit-kumar/PRODIGY_DS_04.git

2. Explore with Jupyter Notebook:   - Open PRODIGY_DS_04.ipynb in Jupyter Notebook for an interactive experience.

Inferences

• Data Exploration  - Uncovered patterns and trends in the Twitter dataset.  - Visualizations provide insights into tweet content and engagement.

• Sentiment Analysis  - Applied sentiment analysis to understand the emotional tone of tweets.  - Visualized sentiment distribution and patterns.

Feel free to delve into the provided analyses, explore visualizations, and gain insights into the sentiment of tweets!

🤝 Contributing

Contributions are encouraged! Whether you want to enhance the analysis or add new features:

• Open issues to discuss potential changes.
• Submit pull requests to collaborate on improvements.

📬 Contact

Questions or suggestions? Reach out to me:

• Ankit Kumar
• Email: [email protected]

Explore the sentiments of Twitter, analyze trends, and unlock the power of social media data! 📈🐦