In this guided project, you’ll explore the tools and techniques needed for doing sentiment analysis. Using NLP methods, you’ll preprocess data and employ the VADER sentiment analysis tool to train your model on a diverse set of X (Twitter) customer service tweets. Enhance model accuracy through hyperparameter tuning and leverage the insights gained from VADER to apply XGBoost and logistic regression models, categorizing the emotional tones of tweets into negative, neutral, or positive sentiments.

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A Look at the Project Ahead

• Get hands-on experience with sentiment analysis
• Learn to preprocess the data with natural language processing
• Get hands-on experience with evaluating your model with VADER, XGBoost, and logistic regression algorithms
• Visualize the insights with bar charts and word clouds

What You’ll Need

• No installation required: Everything is available in the JupyterLab, including any Python libraries and data sets.
• Some understanding of Python: Having some understanding of Python is required for some preprocessing text tasks.
• Some understanding of statistical concepts: It’s helpful to have some understanding of statistic concepts, particularly XGBoost and Logistic Regression algorithms.