Exploring text datasets by visualizing relevant words

• Main Authors: Horn, Franziska, Arras, Leila, Montavon, Grégoire, Müller, Klaus-Robert, Samek, Wojciech
• Format: Journal Article
• Language: English
• Published: 17-07-2017
• Subjects: Computer Science - Computation and Language
• DOI: 10.48550/arxiv.1707.05261

When working with a new dataset, it is important to first explore and
familiarize oneself with it, before applying any advanced machine learning
algorithms. However, to the best of our knowledge, no tools exist that quickly
and reliably give insight into the contents of a selection of documents with
respect to what distinguishes them from other documents belonging to different
categories. In this paper we propose to extract `relevant words' from a
collection of texts, which summarize the contents of documents belonging to a
certain class (or discovered cluster in the case of unlabeled datasets), and
visualize them in word clouds to allow for a survey of salient features at a
glance. We compare three methods for extracting relevant words and demonstrate
the usefulness of the resulting word clouds by providing an overview of the
classes contained in a dataset of scientific publications as well as by
discovering trending topics from recent New York Times article snippets.