Module 4: Advanced Techniques
Once you've mastered the basics of text analysis, you can branch into more sophisticated methods. These advanced techniques allow you to uncover deeper insights and better structure in your textual data.
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Topic Modeling
- Latent Dirichlet Allocation (LDA): an unsupervised approach to identify abstract "topics" within a document corpus
- Assigns each document a mixture of topics and each topic a set of words
- Useful for summarizing large corpora and discovering underlying themes
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Named Entity Recognition (NER)
- Identifies specific entities like people, organizations, and places
- Automates the extraction of structured information from unstructured text
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Machine Learning for Text Classification
- Supervised learning approach: training models (e.g., Naive Bayes, SVM) on labeled data
- Evaluation metrics: precision, recall, F1-score for performance assessment
Short Exercise
- Pick one advanced technique (Topic Modeling, NER, or Text Classification) and briefly describe a research question in your field that could benefit from it. For example, "How can topic modeling help categorize a large corpus of historical texts to identify emerging themes over time?"
Up next, Module 5 will delve into practical tools and implementation strategies for text mining.
Fill out this Google Colab Notebook to practice the techniques in this module.