Topic_Model_Validation_Overview {validateIt}

Topic_Model_Validation Repository Overview

Description

The 'Topic_Model_Validation' repository is a collection of scripts and functions for performing topic modeling and evaluating topic models. This document provides an overview of the different scripts and functions in the repository and their purpose.

Details

## Python Scripts ### evaluate.py The 'evaluate.py' script provides functions for evaluating the performance of topic models on different datasets and tasks. The functions within this script include: - R4WSItasktest(): Evaluates the performance of a topic model on the R4WSI task, which involves predicting the top k words for a given topic. - allR4WSItasktest(): Evaluates the performance of a topic model on multiple versions of the R4WSI task. - goldR4WSItest(): Evaluates the performance of a topic model on a gold-standard R4WSI dataset. - heldouttest(): Evaluates the performance of a topic model on held-out data. - keypostedtest(): Evaluates the performance of a topic model on a key-posted dataset. - masstest(): Evaluates the performance of a topic model on a massive dataset. - modtest(): Evaluates the performance of a topic model on a given dataset. - resultstest(): Evaluates the performance of a topic model on a given dataset and stores the results. ### record.py The 'record.py' script provides a function for storing the results of topic model evaluations. The function within this script is: - record(): Stores the results of topic model evaluations. ## R Scripts ### lda.R The 'lda.R' script provides functions for performing Latent Dirichlet Allocation (LDA) topic modeling on text data. The functions within this script include: - lda_model(): Fits an LDA model to text data. ### lsa.R The 'lsa.R' script provides functions for performing Latent Semantic Analysis (LSA) topic modeling on text data. The functions within this script include: - lsa_model(): Fits an LSA model to text data. ### evaluate.R The 'evaluate.R' script provides functions for evaluating the performance of topic models using various metrics, such as perplexity and coherence. The functions within this script include: - evaluate_model(): Evaluates the performance of a topic model using various metrics. ### helpers.R The 'helpers.R' script provides various helper functions that are used by the other scripts in the repository. The functions within this script include: - clean_text(): Cleans and preprocesses text data for use in topic modeling. - read_data(): Reads in text data from a file. - write_data(): Writes text data to a file.