Sentiment Analysis with Text Mining: A Practical Data Science Capstone Project скачать в хорошем качестве

Sentiment Analysis with Text Mining: A Practical Data Science Capstone Project

Learn how to perform sentiment analysis and text mining using Python in this step-by-step tutorial. We start by loading and preparing real-world customer feedback data, then explore, clean, and visualize the text to uncover key patterns. You will see how to build and evaluate simple sentiment classification models, cluster comments, and even detect anomalies in feedback. This lesson covers practical techniques for analyzing customer comments, including word clouds, association rule mining, supervised classification, unsupervised clustering, ensemble modeling, and anomaly detection. By the end, you will have a solid foundation for applying text mining and sentiment analysis to your own projects. 00:00 Introduction to Sentiment Analysis 00:30 Setting Up the Python Environment 01:20 Loading the Customer Churn Dataset 02:10 Exploring the Data Structure 03:10 Data Cleaning and Pre-processing 04:10 Creating and Adding Feedback Text 05:10 Visualizing Text with Word Clouds 06:40 Basic Association Rule Mining 07:40 Preparing Data for Sentiment Classification 09:00 Splitting Data for Training and Testing 10:00 Building a Naive Bayes Classifier 11:30 Evaluating Model Performance 12:40 Clustering Feedback with K-Means 13:40 Ensemble Voting for Sentiment 15:00 Anomaly Detection in Text 16:00 Recap and Key Takeaways 17:00 Next Steps and Challenge #SentimentAnalysis #TextMining #PythonTutorial