This paper demonstrates how to use the R package stm for structural topic modeling. The structural topic model allows researchers to flexibly estimate a topic model that includes document-level metadata. Estimation is accomplished through a fast variational approximation. The stm package provides many useful features, including rich ways to explore topics, estimate uncertainty, and visualize quantities of interest.

/pdf/stm-an-r-package-for-structural-topic-models-3vfz80akjf.pdf

stm: An R Package for Structural Topic Models

This study examines the level of politicization and polarization in COVID-19 news in U.S. newspapers and televised network news from March to May 2020. Using multiple computer-assisted content analytic approaches, we find that newspaper coverage is highly politicized, network news coverage somewhat less so, and both newspaper and network news coverage are highly polarized. We find that politicians appear in newspaper coverage more frequently than scientists, whereas politicians and scientists are more equally featured in network news. We suggest that the high degree of politicization and polarization in initial COVID-19 coverage may have contributed to polarization in U.S. COVID-19 attitudes.

https://journals.sagepub.com/doi/pdf/10.1177/1075547020950735

Politicization and Polarization in COVID-19 News Coverage

COVID-19 is having a tremendous impact on gender relations, as care needs have been magnified due to schools and day-care closures. Using topic modeling on over 1,100 open reports from a survey fie...

https://www.tandfonline.com/doi/pdf/10.1080/14616696.2020.1808692

Cause for concerns: gender inequality in experiencing the COVID-19 lockdown in Germany

Automated text analysis allows researchers to analyze large quantities of text. Yet, comparative researchers are presented with a big challenge: across countries people speak different languages. To address this issue, some analysts have suggested using Google Translate to convert all texts into English before starting the analysis (Lucas et al. 2015). But in doing so, do we get lost in translation? This paper evaluates the usefulness of machine translation for bag-of-words models—such as topic models. We use the europarl dataset and compare term-document matrices (TDMs) as well as topic model results from gold standard translated text and machine-translated text. We evaluate results at both the document and the corpus level. We first find TDMs for both text corpora to be highly similar, with minor differences across languages. What is more, we find considerable overlap in the set of features generated from human-translated and machine-translated texts. With regard to LDA topic models, we find topical prevalence and topical content to be highly similar with again only small differences across languages. We conclude that Google Translate is a useful tool for comparative researchers when using bag-of-words text models.

/pdf/no-longer-lost-in-translation-evidence-that-google-translate-2k5vjhihtl.pdf

No Longer Lost in Translation: Evidence that Google Translate Works for Comparative Bag-of-Words Text Applications

Sentiment is central to many studies of communication science, from negativity and polarization in political communication to analyzing product reviews and social media comments in other sub-fields...

/pdf/the-validity-of-sentiment-analysis-comparing-manual-umjsc6b3yc.pdf

The Validity of Sentiment Analysis:Comparing Manual Annotation, Crowd-Coding, Dictionary Approaches, and Machine Learning Algorithms

quanteda is an R package providing a comprehensive workflow and toolkit for natural language processing tasks such as corpus management, tokenization, analysis, and visualization. It has extensive functions for applying dictionary analysis, exploring texts using keywords-in-context, computing document and feature similarities, and discovering multi-word expressions through collocation scoring. Based entirely on sparse operations,it provides highly efficient methods for compiling document-feature matrices and for manipulating these or using them in further quantitative analysis. Using C++ and multi-threading extensively, quanteda is also considerably faster and more efficient than other R and Python packages in processing large textual data. The package is designed for R users needing to apply natural language processing to texts,from documents to final analysis. Its capabilities match or exceed those provided in many end-user software applications, many of which are expensive and not open source. The package is therefore of great benefit to researchers, students, and other analysts with fewer financial resources. While using quanteda requires R programming knowledge, its API is designed to enable powerful, efficient analysis with a minimum of steps. By emphasizing consistent design, furthermore, quanteda lowers the barriers to learning and using NLP and quantitative text analysis even for proficient R programmers.

/pdf/quanteda-an-r-package-for-the-quantitative-analysis-of-41v0n3rrl1.pdf

Haiyan Wang

Papers

quanteda: An R package for the quantitative analysis of textual data

kbenoit/quanteda: CRAN v0.9.9.65

Quantitative Analysis of Textual Data [R package quanteda version 2.1.2]

Scaling Models and Classifiers for Textual Data [R package quanteda.textmodels version 0.9.4]