Latent Dirichlet Allocation

Latent Dirichlet Allocation

Latent Dirichlet Allocation

Description

Latent Dirichlet Allocation is one of the popular methods in topic modeling. It is an unsupervised learning algorithm. LDA aims to identify and extract the topics from a large collection of text datasets.  

Why to Use

  • Topic Identification
  • To reduce the dimensionality of the data
  • To understand the underlying structure of data

When to Use

  • On larger collection of text datasets
  • To cluster documents
  • Building search engine or recommendation engine

When Not to Use

  • Non-textual datasets
  • On short texts
  • On frequently updating dataset
  • Datasets with complex hierarchical structures

Prerequisites

  • Split text into individual words
  • Convert the text in lower case
  • Remove stopping words
  • Remove non-alphabetic characters

Input

Preprocessed large text dataset

Output

  • Coherence Scores vs. Number of Topics Chart
  • Assigned Topics
  • Intertopic Distance Map (via multidimensional scaling)
  • Top-30 Most Salient Terms
  • λ Value Table
  • WordCloud Chart

Statistical Methods Used

–

Limitations

  • Predefined Number of Topics
  • Highly Sensitive to Hyperparameters
  • May Overfit the Small Datasets
  • Interpretability of Topics
  • Difficulty with Shorts Texts

Latent Dirichlet Allocation (LDA) is an unsupervised classification algorithm widely used in the Natural Language Processing model. Researchers and Analysts use this method discover the connections in word distribution between many text documents. Each document contains various words and topics, and each topic is associated with some words. LDA aims to identify the topic that the document belongs to, on the basis of these words. This method assumes that the document with similar words will use a similar set of words.

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