Text networks, part three: Dependency networks

This post is part of a series of posts about text network analysis. Each post will discuss one approach to algorithmically creating networks from texts. See the primer for an introduction and the list of posts.

In the previous post, we introduced the basics of text-network analysis using the example of co-occurrence networks. Any application of text-network analysis evolves around two questions: 1. What are my nodes? and 2. what are the relations (edges) between nodes? The answer to the first question is to a large extend the same with different text-network creation methods. The main differences concern the way of identifying edges. Therefore, this post will focus on the second question and only highlight relevant differences in the selection of nodes.

Co-occurrence networks construct edges between nodes based on their proximity in the text. This is a quite rough criterion and error prone: Words close to each other will not always have a meaningful relation to each other, so one gets more edges than desired (false positives). On the other hand, some syntactic arrangements like subordinate clauses can have the effect that related words appear relatively far from each other, and the methods misses relevant edges (false negatives). A better approach thus might take the syntactic structure of the text into account.

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Text networks, part two: Co-occurrence networks

This post is part of a series of posts about text network analysis. Each post will discuss one approach to algorithmically creating networks from texts. See the primer for an introduction and the list of posts.

Recalling the networks 101, a network in its formal representation consists of nodes, i.e. the basic elements of my data, and edges, i.e. relations between the nodes. When creating a network from a text, one has to answer two basic questions:

  1. What are my nodes?
  2. What constitutes a relation between two nodes?

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Text networks, part one: Primer

This post is part of a series of posts about text network analysis. Each post will discuss one approach to algorithmically creating networks from texts.
Historical network analysis—or, more generally, network analysis in the digital humanities—is in most cases an application of Social Network Analysis in the fields of history or literary studies. It shares with sociological approaches its focus on social actors, or: people. In contrast to sociology, DH applications mostly focus on dead (history) or fictional (literary studies) people.
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WhoForShe? Or: tinkering with Open Data

This post is not really related to the SeNeReKo project. But using an unrelated example, it shows the benefits of Open Data: Re-combining existing information to create new insights. This is really what SeNeReKo is about.

Last week, the UN Women launched the campaign “HeForShe” with Emma Watson’s speech at the UN Headquaters. The campaign aims at including men into a “solidarity movement for gender equality”. In order to make this support visible, the campaign website allows men to commit to the campaign’s idea by submitting a web form. Based on the submitted data, the website displays a map of the world, showing (on click) the number of signers.

Map of HeForShe signers Weiterlesen

How Else to Create Lemmatized Text for Topic Modeling

Here it is at last, the first post in the new blog for the SeNeReKo project. On this blog, we will write about aspects of our work, ongoing experiments and lessons learned. Our work heavily builds on the work of others, and we hope it will be of use beyond our project as well. That is why I want to open this blog with a response to another blog post.

Crowds gather to watch cranes joining two ends of an oil pipeline before the official ceremony commemorating the joining of the pipeline of an oil tanker terminal, Portland, Maine, with refineries in Montreal, Quebec.

In his post How to Create Lemmatized (French) Text for Topic Modeling, Christof described how to make use of the TreeTagger output when preparing texts for Topic Modeling. As a by-product of our own work in SeNeReKo, we aimed for a more generic and hopefully simpler approach to deal with annotations of this kind. We chose WebLicht as a starting point, because it allows to easily build annotation toolchains using independent components. Besides others, it supports TreeTagger for lemmatizing French, so generally WebLicht would be a good match for Christof’s task.

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Gods, Graves and Graphs

Das Projekt SeNeReKo ist ein gemeinsames Forschungsvorhaben des Centrums für Religionswissenschaftliche Studien (CERES) an der Ruhr-Universität Bochum und des Trier Center for Digital Humanities (TCDH). In der Zusammenarbeit von Geistes- und Sozialwissenschaften einerseits und Informatik andererseits sollen neue Verfahren der Analyse religionshistorischen Quellenmaterials entwickelt werden. Das Projekt steht damit im Kontext der „Digital Humanities“ und baut auf bisherigen Vorhaben zur Digitalisierung und Erschließung historischer Korpora auf. Ziel ist es, die vorhandenen digitalen Materialien nun für Fragestellungen der Religionswissenschaft fruchtbar zu machen und in diesem Zuge auch neue methodische Verfahren zu entwickeln. Inhaltlich geht das Projekt von Fragen des Religionskontakts aus: Aufbauend auf dem Konzept der „Relational Religion“ und auf Arbeiten des Bochumer KHK zur historischen Entwicklung religiöser Kontaktzonen und zu Prozessen des Religionskontakts sollen der Transfer religiöser Ideen ebenso rekonstruiert werden wie religiöse Konfliktlinien. Weiterlesen