In this video, learn some best practices for processing text data.
- [Instructor] What are some of the key practices … to consider while processing text? … First, filter text data as early as possible in the process. … Text data is heavy and the lighter we make it earlier, … it is easier on resource consumption in the later stages. … Use an exhaustive and context specific stop-word list … to eliminate stop-words. … Stop-words do not carry any insights, … so eliminating most of them is important for efficiency. … Identify domain specific data for special use. … Examples of such strings would be product names, … which occur in text data. … These special words mean a specific purpose for the text … and can be used to index and classify them. … While building TF-IDF matrices, … eliminate tokens that occur rarely. … They usually are not useful in classification or analysis. … Build a clean and indexed corpus … based on the language and business context … persisted for future use. … …
- Text mining today
- Reading text files using Python
- Cleansing text data
- Build n-grams databases for text predictions
- Preparing TF-IDF matrices for machine learning
- Scaling text processing for performance
Skill Level Intermediate
Processing Text with R Essential Trainingwith Kumaran Ponnambalam55m 57s Intermediate
1. Text Mining
2. Reading Text
3. Text Cleansing and Extraction
4. Advanced Text Processing
5. Best Practices
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