计算机科学
变压器
德国的
语言模型
文字嵌入
自然语言处理
人工智能
情绪分析
嵌入
词(群论)
可预测性
政治
语音识别
语言学
数学
哲学
物理
统计
量子力学
电压
政治学
法学
作者
Tobias Widmann,Maximilian Wich
出处
期刊:Political Analysis
[Cambridge University Press]
日期:2022-06-29
卷期号:31 (4): 626-641
被引量:13
摘要
Abstract Previous research on emotional language relied heavily on off-the-shelf sentiment dictionaries that focus on negative and positive tone. These dictionaries are often tailored to nonpolitical domains and use bag-of-words approaches which come with a series of disadvantages. This paper creates, validates, and compares the performance of (1) a novel emotional dictionary specifically for political text, (2) locally trained word embedding models combined with simple neural network classifiers, and (3) transformer-based models which overcome limitations of the dictionary approach. All tools can measure emotional appeals associated with eight discrete emotions. The different approaches are validated on different sets of crowd-coded sentences. Encouragingly, the results highlight the strengths of novel transformer-based models, which come with easily available pretrained language models. Furthermore, all customized approaches outperform widely used off-the-shelf dictionaries in measuring emotional language in German political discourse.
科研通智能强力驱动
Strongly Powered by AbleSci AI