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CLEAN TEXT WITH GENSIM INSTALL
CLEAN TEXT WITH GENSIM UPDATE

We discuss general sources of data and corpora useful for machine learning purposes.

CLEAN TEXT WITH GENSIM INSTALL

We describe the additional Python packages we need for this work, and install and configure the first ones. We provide particular attention to the architecture and data flows within the PyTorch framework. In this particular installment we flesh out the plan for completing these installments and discuss data sources and completing data prep needed for the plan. We conclude this Part VI with a summary and comparison of results across these installments based on the task of node classification. We next devote four installments to deep learning, split equally between the Deep Learning Graph ( DGL) and PyTorch Geometric ( PyG) frameworks. Then we devote two installments to ‘standard’ machine learning (largely) using the scikit-learn packages. We devote two installments to data sources and input preparations, largely based on NLP (natural language processing) applications. We will be devoting our next nine installments to this area. The dimensionality K of Dirichlet distribution (aka # of topics) is assumed to be known and fixed.With our discussions of network analysis and knowledge extractions from our knowledge graph now behind us, we are ready to tackle the questions of analytic applications and machine learning in earnest for our Cooking with Python and KBpedia series. Use these assignments to estimate topic mixtures of each document (\% words assigned to each topic within that document) and word associated to each topic (\% of words assigned to each topic overall).Keep iterating until assignments reach a steady state.

CLEAN TEXT WITH GENSIM UPDATE

Update assignment of current word in the loop assuming topic assignment distributions for the rest of the corpus are correct.Reassign with a new topic, where we choose topic t with probability $p(topic_t|document_d)*p(word_w|topic_t)$, which is essentially the probability that topic t generated word w.$ p(word_w | topic_t)$ = proportion of assignments to topic t over all documents that come from word w (how many w in all documents' words are assigned to t).$ p(topic_t | document_d)$ = proportion of words in document d that are assigned to topic t.To improve, for each document D, go through each word in D:.Because this is random, it will not be good.This gives random assignment of topic representations of all the documents and word distributions of all the topics.

Given a set of documents, go through each document, and randomly assign each word in the document to one of the K topics.

The algorithm uses collapsed Gibbs Sampling:

Find set of topics that are most likely to have generated the collection of documents.

if length is 5, then generate set of 5 words with probability distribution across words for a given topic such as "broccoli eat food animal meat" if select food topic, generate word 'broccoli' with 30\% probability and bananas with 15\% probability The generative process for each document w in corpus D is as belows: It assumes that the topics are generated before documents, and infer topics that could have generated the a corupus of documents (a review = a document). LDA is a type of Bayesian Inference Model. It then approximates probability distributions of topics in a given document and of words in a given topic. LDA assumes each document consists of a combination of topics, and each topic consists of a combination of words. A common method to quickly understand trends in topics being discussed in a corpus of text is Latent Dirichlet Allocation (LDA). Businesses can benefit immensely if they can understand general trends of what their customers are talking about online.

With so many online reviews across many social media websites, it is hard for companies to keep track of their online reputation.