Van Rensbergen Bram, De Deyne Simon, Storms Gert
Laboratory of Experimental Psychology, University of Leuven, Tiensestraat 102 B3711, 3000, Leuven, Belgium.
Computational Cognitive Science Laboratory, University of Adelaide, Adelaide, SA, Australia.
Behav Res Methods. 2016 Dec;48(4):1644-1652. doi: 10.3758/s13428-015-0680-2.
Word ratings on affective dimensions are an important tool in psycholinguistic research. Traditionally, they are obtained by asking participants to rate words on each dimension, a time-consuming procedure. As such, there has been some interest in computationally generating norms, by extrapolating words' affective ratings using their semantic similarity to words for which these values are already known. So far, most attempts have derived similarity from word co-occurrence in text corpora. In the current paper, we obtain similarity from word association data. We use these similarity ratings to predict the valence, arousal, and dominance of 14,000 Dutch words with the help of two extrapolation methods: Orientation towards Paradigm Words and k-Nearest Neighbors. The resulting estimates show very high correlations with human ratings when using Orientation towards Paradigm Words, and even higher correlations when using k-Nearest Neighbors. We discuss possible theoretical accounts of our results and compare our findings with previous attempts at computationally generating affective norms.
情感维度上的词汇评级是心理语言学研究中的一项重要工具。传统上，这些评级是通过要求参与者对每个维度的词汇进行评级来获得的，这是一个耗时的过程。因此，人们对通过计算生成规范产生了一些兴趣，即利用单词与已知情感评级的单词之间的语义相似性来推断单词的情感评级。到目前为止，大多数尝试都是从文本语料库中的单词共现中得出相似性的。在本文中，我们从单词联想数据中获取相似性。我们使用这些相似性评级，借助两种推断方法：范式词导向法和k近邻法，来预测14000个荷兰语单词的效价、唤醒度和支配性。当使用范式词导向法时，所得估计值与人类评级显示出非常高的相关性，而使用k近邻法时相关性更高。我们讨论了对结果的可能理论解释，并将我们的发现与之前计算生成情感规范的尝试进行了比较。
