On the Usefulness of Personality Traits in Opinion-oriented Tasks
On the Usefulness of Personality Traits in Opinion-oriented Tasks
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DOI：
10.26615/978-954-452-072-4_062
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Marjan Hosseinia;E. Dragut;Dainis Boumber;Arjun Mukherjee
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文献类型：
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作者：
Marjan Hosseinia;E. Dragut;Dainis Boumber;Arjun Mukherjee
我们使用一个深度双向Transformer从多标签和多类分类设置中的用户生成的数据中提取Myers-Briggs个性类型。我们的数据集很大，由Reddit、Twitter和Personality Cafe论坛等各种社交媒体平台的三个可用个性数据集组成。我们从基于transformer的模型中推导出个性嵌入，并研究它们是否可用于下游文本分类任务。实验证据表明，人格嵌入是有效的三个分类任务，包括作者身份验证，立场，超党派检测。我们还为第三项任务：超党派新闻分类提供了新颖且可解释的分析。
We use a deep bidirectional transformer to extract the Myers-Briggs personality type from user-generated data in a multi-label and multi-class classification setting. Our dataset is large and made up of three available personality datasets of various social media platforms including Reddit, Twitter, and Personality Cafe forum. We induce personality embeddings from our transformer-based model and investigate if they can be used for downstream text classification tasks. Experimental evidence shows that personality embeddings are effective in three classification tasks including authorship verification, stance, and hyperpartisan detection. We also provide novel and interpretable analysis for the third task: hyperpartisan news classification.