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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.05990 |
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| _version_ | 1866916773237882880 |
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| author | Duan, Zening Shao, Anqi Hu, Yicheng Lee, Heysung Liao, Xining Suh, Yoo Ji Kim, Jisoo Yang, Kai-Cheng Chen, Kaiping Yang, Sijia |
| author_facet | Duan, Zening Shao, Anqi Hu, Yicheng Lee, Heysung Liao, Xining Suh, Yoo Ji Kim, Jisoo Yang, Kai-Cheng Chen, Kaiping Yang, Sijia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_05990 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Constructing Vec-tionaries to Extract Message Features from Texts: A Case Study of Moral Appeals Duan, Zening Shao, Anqi Hu, Yicheng Lee, Heysung Liao, Xining Suh, Yoo Ji Kim, Jisoo Yang, Kai-Cheng Chen, Kaiping Yang, Sijia Computation and Language 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. |
| title | Constructing Vec-tionaries to Extract Message Features from Texts: A Case Study of Moral Appeals |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2312.05990 |