- Text Network Analysis: Generate Beautiful Network Visualisations
- Introduction
- Best practices for text network visualization
- #1: Start with a goal
- textnets 0.9.3
- Features
- Citation
- Textnets: text analysis with networks¶
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- Text Network Analysis: Theory and Practice
- Text network analysis belongs to the broader skill set of most text data-oriented analysts.
- Text Network Analysis
Text Network Analysis: Generate Beautiful Network Visualisations
Discover how to visualize text networks in the circular, radial, and matrix forms: circos, hive, and matrix plots. At the same time, learn the dos and don’ts of plotting text networks.
Introduction
This article develops a series on text network analysis in Python. It builds on Text Network Analysis: Theory and Practice, laying down the fundamentals of text networks, and Text Network Analysis: A Concise Review of Network Construction Methods, focusing on data preparation and network construction strategies.
The last article split the strategies for constructing a text network into (i) net is drawn from the raw text, and (ii) the net is built on the pre-processed data with the edges and nodes clearly defined. In this article, we will expand the second option and use the pre-processed classic IMDb 50K Movie Reviews (see the data license) to illustrate the following network graphics:
- circos plot: to visualize a network in a circular layout
- hive plot: to structure the network graph along several axes
- matrix plot: to plot a net in its matrix form.
We will not cover the standard network diagram but rather focus on more advanced plots. Pyvis is a very handy Python library that enables the interactive configuration of graphic widgets and can be effectively used here. This tutorial by Khuyen Tran provides an excellent introduction. Also, arc plot, which forms the basis of circos, can intuitively be developed on text networks.
Best practices for text network visualization
In the first step, let’s summarize a couple of dos and don’ts for network visualization that generally apply to most network structures, not only text networks. Although selective and primarily based on personal experience, following them helps avoid some major pitfalls in plotting network data.
#1: Start with a goal
Network data structures can be very complex. To make the visualization job successful, figure out clearly at the beginning (1) the story you want to tell with the graphics and (2) which relationships you want to highlight.
textnets 0.9.3
textnets represents collections of texts as networks of documents and words. This provides novel possibilities for the visualization and analysis of texts.
textnets is free software under the terms of the GNU General Public License v3.
The ideas underlying textnets are presented in this paper:
Initially begun as a Python implementation of Chris Bail’s textnets package for R, textnets now comprises several unique features for term extraction and weighing, visualization, and analysis.
Features
textnets builds on spaCy, a state-of-the-art library for natural-language processing, and igraph for network analysis. It uses the Leiden algorithm for community detection, which is able to perform community detection on the bipartite (word–group) network.
textnets is installable using the conda , pip and nix package managers. It requires Python 3.8 or higher.
textnets integrates seamlessly with Python’s excellent scientific stack. That means that you can use textnets to analyze and visualize your data in Jupyter notebooks!
Read the documentation to learn more about the package’s features.
Citation
Using textnets in a scholarly publication? Please cite this paper:
ython Package for Text Analysis with Networks>
Textnets: text analysis with networks¶
textnets represents collections of texts as networks of documents and words. This provides novel possibilities for the visualization and analysis of texts.
textnets is free software under the terms of the GNU General Public License v3.
The ideas underlying textnets are presented in this paper:
Christopher A. Bail, “Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media,” Proceedings of the National Academy of Sciences of the United States of America 113, no. 42 (2016), 11823–11828, doi:10.1073/pnas.1607151113.
Initially begun as a Python implementation of Chris Bail’s textnets package for R, textnets now comprises several unique features for term extraction and weighing, visualization, and analysis.
Features¶
textnets builds on spaCy, a state-of-the-art library for natural-language processing, and igraph for network analysis. It uses the Leiden algorithm for community detection, which is able to perform community detection on the bipartite (word–group) network.
textnets is installable using the conda , pip and nix package managers. It requires Python 3.8 or higher.
textnets integrates seamlessly with Python’s excellent scientific stack. That means that you can use textnets to analyze and visualize your data in Jupyter notebooks!
Read the documentation to learn more about the package’s features.
Citation¶
Using textnets in a scholarly publication? Please cite this paper:
@articleBoy2020, author = , title = , subtitle = ython Package for Text Analysis with Networks>, journal = , volume = , number = , pages = , year = , doi = , > Learn More¶
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Part of the Data Science course project dedicated to Digital Ethics concepts mapping
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README.md
Digital Ethics concepts mapping
How the concept space of the Digital Ethics research looks like?
The aim of the research is to identify the range of the topics around Digital Ethics studies.
notebook.ipynb : Python code with preprocessing and cleaning the text, visualisation of the discourse and creation of the dataframe for network analysis
The data consists of two files:
Articles for each topic connected with Digital Ethics studies: AI, Robotics, Open Government, Open Source, Accessibility. Articles were collected through Google Scholar, all texts were publicly available and free of charge. For each category, 10 of the most recent articles were collected, which included an ethical framework for study of Tech.
Creating the dataframe for the network analysis
Additional analysis of the network
Exploration of the Digital Ethics discourse
About
Part of the Data Science course project dedicated to Digital Ethics concepts mapping
Text Network Analysis: Theory and Practice
Text network analysis belongs to the broader skill set of most text data-oriented analysts.
This article begins a journey to discover this wonderful area: from theory, data prep, and network construction to visualization and forecasting — this series covers the most fundamental concepts of text networks in Python.
Developments since the late 20th century, such as…
- availability of data from social networks (e.g. Twitter),
- transcripts of politicians’ statements and central bankers’ meetings,
- publicly available APIs to text databases (Google Trends, RSS feeds, Wikipedia, Google Ngrams)
- general development of technologies to process big data
…and potentially many other factors have resulted in a vast amount of text data easily accessible to analysts, students, and researchers. Over time, scientists developed numerous complex methods to understand the relations in the text datasets, including text network analysis.
This first article on text network analysis in Python will briefly survey the underpinnings of text network analysis, real-world applications of text networks, and their implementation in major data science and business intelligence (BI) software.
Text Network Analysis
In the academic literature, networks are more formally referred to as graphs. More rigorous theoretical propositions of graph theory can be traced back to the 1950s (Berge, 1958). Over time, text network literature has evolved into several streams:
- semantic networks: modeling inter-connections of concepts, topics, and keywords (Netzer et al., 2012; Griffiths et al., 2007)
- graph neural networks: combining neural networks with network data structures (Liao et al., 2021; Krenn & Zeilinger, 2020; Yao et al.,2019)
- network visualization methods: proposing new methods of network visualization and graph discovery (Paranyushkin, 2019; Celardo & Everett, 2020)
- software development and algorithm implementation: see the examples in the third section below.