Semeraro Alfonso, Vilella Salvatore, Improta Riccardo, De Duro Edoardo Sebastiano, Mohammad Saif M, Ruffo Giancarlo, Stella Massimo
Department of Computer Science, University of Turin, Turin, Italy.
Dipartimento di Scienze e Innovazione Tecnologica, University of Eastern Piedmont, Alessandria, Italy.
Behav Res Methods. 2025 Jan 27;57(2):77. doi: 10.3758/s13428-024-02553-7.
We introduce EmoAtlas, a computational library/framework extracting emotions and syntactic/semantic word associations from texts. EmoAtlas combines interpretable artificial intelligence (AI) for syntactic parsing in 18 languages and psychologically validated lexicons for detecting the eight emotions in Plutchik's theory. We show that EmoAtlas can match or surpass transformer-based natural language processing techniques, BERT or large language models like ChatGPT 3.5 or LLaMAntino, in detecting emotions from Italian and English online posts and news articles (e.g., achieving 85.6 accuracy in detecting anger in posts vs the 68.8 value of ChatGPT and 89.9% value for BERT). EmoAtlas presents important advantages in terms of speed and absence of fine-tuning, e.g., it runs 12x faster than BERT on the same data. Testing EmoAtlas' and easily trainable transformers' relevance in a psychometric task like reproducing human creativity ratings for 1071 short texts, we find that EmoAtlas and BERT obtain equivalent predictive power (fourfold cross-validation, , ). Combining BERT's semantic features with EmoAtlas' emotional/syntactic networks of words gets substantially better at estimating creativity rates of stories ( , ). This indicates an interplay between the creativity of narratives and their semantic, emotional, and syntactic structure. Via interpretable emotional profiles and syntactic networks, EmoAtlas can also quantify how emotions are channeled through specific words in texts, e.g., how did customers frame their ideas and emotions towards "beds" in hotel reviews? We release EmoAtlas as a standalone "text as data" computational tool and discuss its impact in extracting interpretable and reproducible insights from texts.
我们介绍了EmoAtlas，这是一个从文本中提取情感以及句法/语义单词关联的计算库/框架。EmoAtlas结合了用于18种语言句法解析的可解释人工智能（AI）和用于检测普卢契克理论中八种情感的经过心理验证的词汇表。我们表明，在从意大利语和英语在线帖子及新闻文章中检测情感方面，EmoAtlas能够匹配或超越基于Transformer的自然语言处理技术、BERT或诸如ChatGPT 3.5或LLaMAntino之类的大语言模型（例如，在检测帖子中的愤怒情绪时达到85.6%的准确率，而ChatGPT为68.8%，BERT为89.9%）。EmoAtlas在速度和无需微调方面具有重要优势，例如，在相同数据上它的运行速度比BERT快12倍。在一项心理测量任务（如对1071篇短文本进行人类创造力评分）中测试EmoAtlas和易于训练的Transformer的相关性时，我们发现EmoAtlas和BERT具有同等的预测能力（四重交叉验证， ， ）。将BERT的语义特征与EmoAtlas的情感/句法单词网络相结合，在估计故事的创造力评分方面有显著提升（ ， ）。这表明叙事的创造力与其语义、情感和句法结构之间存在相互作用。通过可解释的情感概况和句法网络，EmoAtlas还可以量化情感如何通过文本中的特定单词传递，例如，在酒店评论中，顾客是如何表达他们对“床”的想法和情感的？我们将EmoAtlas作为一个独立的“文本即数据”计算工具发布，并讨论其在从文本中提取可解释和可重复见解方面的影响。
