Today, there is a range of computer-aided techniques to convert text into data. However, they convey not only strengths but also vulnerabilities compared to traditional content analysis. One of the challenges that have gained increasing attention is performing automatic language analysis to make sound inferences in a multilingual assessment setting. The current study sought to test the equivalence of multiple versions of one of the most appealing and widely used lexicon-based tools worldwide, Linguistic Inquiry and Word Count 2015 (LIWC2015; Pennebaker, Boyd, Jordan, & Blackburn, 2015), which is a topic insufficiently approached. For this purpose, we employed supervised learning in a classification problem and computed Pearson’s correlations on a large corpus of parallel texts in English, Dutch, Brazilian Portuguese, and Romanian. Our findings suggested that LIWC2015 is a valuable tool for multilingual analysis, but within-language standardization is needed when the aim is to analyze texts sourced from different languages.

中文翻译：

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使用 Linguistic Inquiry and Word Count 2015 (LIWC2015) 进行多语言分析。四种语言的等价研究

今天，有一系列计算机辅助技术可以将文本转换为数据。然而，与传统的内容分析相比，它们不仅传达了优势，还传达了弱点。越来越受到关注的挑战之一是执行自动语言分析以在多语言评估环境中进行合理的推断。当前的研究试图测试全球最具吸引力和广泛使用的基于词典的工具之一的多个版本的等效性，语言查询和字数 2015（LIWC2015；Pennebaker、Boyd、Jordan 和 Blackburn，2015），这是一个话题不够深入。为此，我们在分类问题中采用了监督学习，并在英语、荷兰语、巴西葡萄牙语和罗马尼亚语的大型平行文本语料库上计算了 Pearson 相关性。