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Topic Modeling using Latent Dirichlet Allocation (LDA)

This repository presents a simple application of the Latent Dirichlet Allocation (LDA) model for topic modeling. The project consists of applying the LDA model to the document collection of the GT-8 ENANCIB (Information and Technology of the National Meeting of Research in Information Science).

Collection

  • The corpus consists of 273 documents (full articles and extended abstracts between 2012 and 2019).

Requirements

  • Python3
  • python3.8-venv

Exploratory Data Analysis

  • Wordcloud

Data pre-processing

  • Conversion of terms
  • Stopwords configuration
  • Bigram e trigram models
  • Tokenization

What can still be done

  • Removal of words less than 3 characters
  • Removal of irrelevant words
  • Stemming e Lemmatization

How to run

  • Start the project by preparing the environment through the command:
$ source prepare-env.sh
  • Now you can use the notebook TopicModel_LDA in your prefer code editor.