Advances in natural language processing provide accessible approaches to analyze psychological open‐ended data. However, comprehensive instruments for text analysis of stereotype content are missing. We developed stereotype content dictionaries using a semi‐automated method based on WordNet and word embeddings. These stereotype content dictionaries covered over 80% of open‐ended stereotypes about salient American social groups, compared to 20% coverage from words extracted directly from the stereotype content literature. The dictionaries showed high levels of internal consistency and validity, predicting stereotype scale ratings and human judgments of online text. We developed the R package Semi‐Automated Dictionary Creation for Analyzing Text (SADCAT; https://github.com/gandalfnicolas/SADCAT) for access to the stereotype content dictionaries and the creation of novel dictionaries for constructs of interest. Potential applications of the dictionaries range from advancing person perception theories through laboratory studies and analysis of online data to identifying social biases in artificial intelligence, social media, and other ubiquitous text sources.

中文翻译：

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使用半自动化方法的综合刻板印象内容字典

自然语言处理的进步为分析开放式心理数据提供了可访问的方法。但是，缺少用于构造型内容的文本分析的综合工具。我们使用基于WordNet和单词嵌入的半自动化方法开发了原型内容字典。这些刻板印象内容词典涵盖了80％以上针对美国显着社会群体的不限成员名额刻板印象，而直接从刻板印象内容文献中提取的单词所占比例则为20％。字典显示出很高的内部一致性和有效性，预测了刻板印象等级等级和人类对在线文本的判断。我们开发了R包用于文本分析的半自动词典创建（SADCAT; https://github.com/gandalfnicolas/SADCAT），以访问原型内容字典并为感兴趣的结构创建新颖的字典。词典的潜在应用范围从先进的人的感知理论到实验室研究和对在线数据的分析，再到识别人工智能，社交媒体和其他普遍存在的文本来源中的社会偏见。