Offered By: IBMSkillsNetwork
Discover sentiments in customer service tweets
Dive into sentiment analysis using natural language processing (NLP) techniques. Classify tweets by using VADER, XGBoost, and logistic regression algorithms to uncover insights from textual data, offering valuable perspectives on sentiment trends. Learn how to create engaging visualizations like word clouds and bar charts to enhance your understanding of sentiment analysis results.
Continue readingGuided Project
Data Science
At a Glance
Dive into sentiment analysis using natural language processing (NLP) techniques. Classify tweets by using VADER, XGBoost, and logistic regression algorithms to uncover insights from textual data, offering valuable perspectives on sentiment trends. Learn how to create engaging visualizations like word clouds and bar charts to enhance your understanding of sentiment analysis results.
Generated with AI
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.