Text Mining & Natural Language Processing

Exploring the power of computational linguistics and text analysis to extract insights from unstructured data

Text Mining Research

Welcome to my text mining research portfolio. Through these projects, I've explored various techniques for extracting meaningful patterns and insights from text data, with a focus on environmental discourse analysis and climate change communications.

Text mining combines techniques from natural language processing, machine learning, statistics, and data visualization to discover hidden patterns and relationships in unstructured text. My projects demonstrate the application of these techniques to real-world datasets and research questions.

Text Mining Techniques

My research employs a variety of computational methods to analyze text data. Each technique offers unique insights into the structure, content, and meaning of text.

Unsupervised Learning

Discovering hidden patterns and structures in text without labeled data

Clustering Topic Modeling (LDA) Association Rule Mining

Supervised Learning

Classifying text into predefined categories using labeled training data

Classification Overview Naive Bayes Decision Trees Support Vector Machines

Neural Networks

Deep learning approaches for complex language understanding tasks

Neural Network Models

Visualizations

Graphical representation of text data and analysis results

LDA Visualization

Explore the Code

All code, datasets, and detailed documentation for these text mining projects are available in my GitHub repository. Feel free to explore, fork, or contribute to these projects.

View on GitHub

Read My Project Conclusions

Curious about what I've learned from this text mining journey? Check out my non-technical reflections and insights.

Explore Project Conclusions