This project provides a streamlined, standardized way to analyze sensory references in natural speech.
The project started as a collaboration between the departments of Neurobiology and Linguistics at the University of Chicago. More information about the project will be available when the original study is published.
If you're new to Python and Natural Language Processing, the following tutorials offer a structured approach, ranging from basic introductions to advanced topics. They covers all the theory and implementation steps for SensorySpeech. All tutorials can be downloaded in the intro-to-python-and-nlp page.
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Comprehensive Introduction to Python: A thorough introduction for those new to Python.
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Basic Elements of NLP (Part I): Dive into fundamental NLP topics like text cleaning, pre-processing pipelines (including word tokenization, case-folding, stop word removal, stemming, lemmatization, punctuation, and sentence tokenization), and feature extraction techniques such as POS-tagging and vectorization.
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Word Embeddings (Part II): An exploration of word embeddings, including popular methods like Word2Vec and transformers.
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Advanced NLP Techniques (Part III): An optional module that covers hyperparameter tuning, dimensionality reduction, and clustering, especially in the context of topic modeling.
Feel free to explore them sequentially or jump into any specific topic that piques your interest.