ABSTRACT. Many research questions in political communication can be answered by representing text as a network of positive or negative relations between actors and issues such as conducted by semantic network analysis. This article presents a system for automatically determining the polarity (positivity/negativity) of these relations by using techniques from sentiment analysis. We used a machine learning model trained on the manually annotated news coverage of the Dutch 2006 elections, collecting lexical, syntactic, and word-similarity based features, and using the syntactic analysis to focus on the relevant part of the sentence. The performance of the full system is significantly better than the baseline with an F1 score of.63. Additionally, we replicate four studies from an earlier analysis of these elections, attaining correlations of greater than.8 in three out of four cases. This shows that the presented system can be immediately used for a number of analyses.

Dealing with the vast quantities of text that students generate in a Massive Open Online Course (MOOC) is a daunting challenge. Computational tools are needed to help instructional teams uncover themes and patterns as MOOC students write in forums, assignments, and surveys. This paper introduces to the learning analytics community the Structural Topic Model, an approach to language processing that can (1) find syntactic patterns with semantic meaning in unstructured text, (2) identify variation in those patterns across covariates, and (3) uncover archetypal texts that exemplify the documents within a topical pattern. We show examples of computationally-aided discovery and reading in three MOOC settings: mapping students ’ self-reported motivations, identifying themes in discussion forums, and uncovering patterns of feedback in course evaluations.

Abstract Comparative politics scholars are well poised to take advantage of recent advances in research designs and research tools for the systematic analysis of textual data. This paper provides the first focused discussion of these advances for scholars of comparative politics, though many arguments are applicable across political science sub-fields. With the explosion of textual data in countries around the world, it is important for comparativists to stay at the cutting edge. We situate recent and existing tools within a broader framework of methods to process, manage, and analyze textual data. While we review a variety of analysis tools of interest, we particularly focus on methods that take into account information about when and who generated a particular piece of text. We also engage with more pragmatic considerations about the ability to process large volumes of text that come in multiple languages. All of our discussions are illustrated with existing, and several new, software implementations. 0

Edited by Betsy Sinclair Recent advances in research tools for the systematic analysis of textual data are enabling exciting new research throughout the social sciences. For comparative politics, scholars who are often interested in nonEnglish and possibly multilingual textual datasets, these advances may be difficult to access. This article discusses practical issues that arise in the processing, management, translation, and analysis of textual data with a particular focus on how procedures differ across languages. These procedures are combined in two applied examples of automated text analysis using the recently introduced Structural Topic Model. We also show how the model can be used to analyze data that have been translated into a single language via machine translation tools. All the methods we describe here are implemented in open-source software packages available from the authors.

This paper applies the homophily thesis to public diplomacy and offers an empirical examination of a country’s success in its mediated public diplomacy efforts. It analyzes international frame building, the process of creating or changing media frames in the international communications arena, by applying it to the case of Israeli mediated public diplomacy efforts during the war in Gaza in the winter of 2008-2009. The paper claims that one way to use the homophily thesis in empirical analyses of international frame building campaigns in conflicts is to measure the political and value proximity of a country promoting frames to other countries. Yet proximity should be measured relatively rather than in absolute terms. Therefore, one should look not only at the dyadic proximity between two actors (i.e., Country A that attempts to promote its frames to Country C), but rather at the relative proximity between Countries A and C considering the proximity between the rival Country B and the target Country C. The study proposes a model and a method to facilitate empirical analysis of this claim. Using sophisticated computerized content analysis, our analyses demonstrated that relative proximity was related to successful international frame building in the hypothesized direction: the closer the relative proximity between Israel and a foreign country, the greater the acceptance of Israel’s views.

This paper is part of a series of method papers on the use of advanced text analytical methods in the political and social sciences. The papers where presented at the