This is not a module for large scale use, but rather a set of scripts to explain popular methodologies in text analysis, including Web Scraping, Preprocessing, Skip Gram (word2vec), and Topic Modelling.
How can I download text data from a website algorithmically using Python? How do I store the data in a csv file for later use?
Web_Scraping.py: explains how to download movie quotes and store the data neatly in a table using the Pandas Python module.
How are documents and words represented in Python? How can I clean text in Python by removing unnecessary words and adjusting for infrequent words?
Text_Preprocessing.py: explains common ways of representing text data in Python through one-hot encoded vectors, cleaning data with removal of stopwords and lowercasing, and TF-IDF weights.
How can I discover topics of documents? I.e. how can I calculate how much one article is about sports, another about business, etc.?
EM_Algorithm.py: explains how to estimate a distribution using the EM-Algorithm. This is a precursor to the topic modelling example.
How can I discover topics of documents? I.e. how can I calculate how much one article is about sports, another about business, etc.?
Gibbs_Sampling.py: explains how Gibbs sampling works in the context of topic modelling.
How can I find which words in my documents are related to each other syntactically and semantically? How does a basic neural network work?
Skip_Gram.py: explains how the Skip Gram model from Mikolov et al. works (with gradient descent and no negative sampling).
How can I develop a language model with memory? How does backpropogation/gradient descent through time work?
LSTM_Tutorial.py: explains the backpropogation of an LSTM model. Extends the code from Nicolas Jimenez to train a language model with memory.
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