But researchers interested in cross-country comparisons face a problem: people speak different languages. Many researchers have noticed its potential and are now using methods such as topic modeling, scaling and sentiment analysis to analyze political texts (for an overview see Grimmer and Stewart Reference Grimmer and Stewart2013). We conclude that Google Translate is a useful tool for comparative researchers when using bag-of-words text models.Īutomated text analysis is like a gold rush. With regard to LDA topic models, we find topical prevalence and topical content to be highly similar with again only small differences across languages. What is more, we find considerable overlap in the set of features generated from human-translated and machine-translated texts. We first find TDMs for both text corpora to be highly similar, with minor differences across languages. We evaluate results at both the document and the corpus level. 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. 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. 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). Yet, comparative researchers are presented with a big challenge: across countries people speak different languages. Automated text analysis allows researchers to analyze large quantities of text.
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