Module 1: Introduction to Text Mining
Text mining (also known as text analytics) combines methods from natural language processing, machine learning, and statistics to derive insights from large volumes of unstructured text data. In this module, you will learn the core definitions, the typical workflow, and the key applications of text mining.
Here are the main areas of focus:
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Definition
- Text mining refers to extracting meaningful patterns and information from textual data.
- It applies automated techniques to handle the unstructured nature of text.
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Why Text Mining?
- Massive volumes of text data are produced every day (emails, tweets, articles, etc.).
- Automating analysis saves time and reveals patterns not easily found by manual reading.
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Common Applications
- Sentiment Analysis (e.g., brand perception on social media)
- Topic Modeling (e.g., discovering themes in a collection of articles)
- Document Classification (e.g., sorting news articles into categories)
- Named Entity Recognition (e.g., identifying people, places, organizations)
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The Text Mining Workflow
- Data Collection: Gather text data (from websites, APIs, databases, etc.)
- Preprocessing: Clean and prepare data (remove noise, tokenization, etc.)
- Analysis: Apply statistical/NLP methods (word frequency, sentiment, topic modeling)
- Visualization: Present findings (word clouds, bar charts, etc.)
- Interpretation & Reporting: Make sense of results and communicate insights
Short Exercise
- Think of three areas in your field of study where text mining could be applied. What specific questions would you want to answer?
Once you understand these introductory concepts, you can move on to Module 2 where you will learn about data preparation and cleaning.
Fill out this Google Colab Notebook to practice the techniques in this module.