Packaged: 2017-10-15 07:02:59 UTC; szepannek

Packaged: 2018-09-05 15:06:11 UTC; szepannek

Author: Gero Szepannek [aut, cre], Rabea Aschenbruck [aut]
Packaged: 2019-03-16 15:58:44 UTC; szepannek

Packaged: 2021-03-09 14:09:05 UTC; szepannek

Packaged: 2021-07-14 09:25:13 UTC; szepannek

Packaged: 2021-08-18 08:01:14 UTC; szepannek

Packaged: 2020-03-04 09:25:06 UTC; szepannek

Packaged: 2020-04-22 20:50:18 UTC; szepannek

Packaged: 2020-11-04 09:38:19 UTC; szepannek

Title: Easy Interface for Clustering a Set of Documents and Exploring
Description: Provides an interface to perform cluster analysis on a corpus of text. Interfaces to
Quanteda to assemble text corpuses easily. Deviationalizes text vectors prior to clustering
using technique described by Sherin (Sherin, B. [2013]. A computational study of commonsense science:
An exploration in the automated analysis of clinical interview data. Journal of the Learning Sciences,
metric for two stage clustering process, involving Ward's algorithm hierarchical agglomerative clustering,
and k-means clustering. Selects optimal number of clusters to maximize "variance explained" by clusters,
adjusted by the number of clusters. Provides plotted output of clustering results as well as printed output.
Assesses "model fit" of clustering solution to a set of preexisting groups in dataset.
Imports: quanteda, dplyr, ggplot2, ppls, tidyr
Packaged: 2017-01-06 19:59:18 UTC; joshuarosenberg

Author: Joshua Rosenberg, Alex Lishinski
Description: Provides an interface to perform cluster analysis on a corpus of
text. Interfaces to Quanteda to assemble text corpuses easily. Deviationalizes
text vectors prior to clustering using technique described by Sherin (Sherin,
B. [2013]. A computational study of commonsense science: An exploration in the
automated analysis of clinical interview data. Journal of the Learning Sciences,
cosine similarity as distance metric for two stage clustering process, involving
Ward's algorithm hierarchical agglomerative clustering, and k-means clustering.
Selects optimal number of clusters to maximize "variance explained" by clusters,
adjusted by the number of clusters. Provides plotted output of clustering
results as well as printed output. Assesses "model fit" of clustering solution
to a set of preexisting groups in dataset.