Constructing Vec-tionaries to Extract Message Features from Texts: A Case Study of Moral Appeals

• URL: http://arxiv.org/abs/2312.05990v2
• Date: Sat, 9 Mar 2024 04:55:47 GMT
• Title: Constructing Vec-tionaries to Extract Message Features from Texts: A Case Study of Moral Appeals
• Authors: Zening Duan, Anqi Shao, Yicheng Hu, Heysung Lee, Xining Liao, Yoo Ji Suh, Jisoo Kim, Kai-Cheng Yang, Kaiping Chen, and Sijia Yang
• Abstract summary: We present an approach to construct vec-tionary measurement tools that boost validated dictionaries with word embeddings. A vec-tionary can produce additional metrics to capture the ambivalence of a message feature beyond its strength in texts.
• Score: 5.336592570916432
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: While researchers often study message features like moral content in text, such as party manifestos and social media, their quantification remains a challenge. Conventional human coding struggles with scalability and intercoder reliability. While dictionary-based methods are cost-effective and computationally efficient, they often lack contextual sensitivity and are limited by the vocabularies developed for the original applications. In this paper, we present an approach to construct vec-tionary measurement tools that boost validated dictionaries with word embeddings through nonlinear optimization. By harnessing semantic relationships encoded by embeddings, vec-tionaries improve the measurement of message features from text, especially those in short format, by expanding the applicability of original vocabularies to other contexts. Importantly, a vec-tionary can produce additional metrics to capture the valence and ambivalence of a message feature beyond its strength in texts. Using moral content in tweets as a case study, we illustrate the steps to construct the moral foundations vec-tionary, showcasing its ability to process texts missed by conventional dictionaries and word embedding methods and to produce measurements better aligned with crowdsourced human assessments. Furthermore, additional metrics from the vec-tionary unveiled unique insights that facilitated predicting outcomes such as message retransmission.

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