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Topic Modeling with Spark and LDA to Facilitate Legal Investigations Ryan Chamberlain, Arthur Mak, James Route, Sayantan Satpati chamberlain.ryan@gmail.com, arthur.t.mak@gmail.com, james.route@gmail.com,
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Topic modeling with Spark refers to the use of Apache Spark, a distributed computing framework, to automatically identify and extract topics from large collections of text data. It employs algorithms like Latent Dirichlet Allocation (LDA) to uncover hidden thematic structures in text.
There is no requirement to 'file' topic modeling with Spark; rather, it is a computational technique used by data scientists and analysts to analyze text data. Organizations working with large datasets may implement such models for insights.
To perform topic modeling with Spark, one typically needs to prepare text data, tokenize the text, use the MLlib library in Spark for model training, and then interpret the output topics. Steps include loading data, preprocessing, fitting the model, and visualizing results.
The purpose of topic modeling with Spark is to discover abstract topics that occur in a collection of documents, providing valuable insights into the structure and themes present in large amounts of text data.
Typically, the output of topic modeling with Spark includes the identified topics, their associated keywords, and the distribution of topics across the documents. Specific reporting requirements may vary depending on the use case or regulatory requirements.
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