ABSTRACT

Automatic sentiment analysis is used extensively in political science. The digitization of legislative transcripts has increased the potential application of established tools for the automated analyses of emotion in text. Unlike in writing, however, expressing emotion in speech involves intonation, facial expressions, and body language. Drawing on a new dataset of annotated texts and videos from the Canadian House of Commons, this paper does three things. First, we examine whether transcripts capture the emotional content of speeches. We find that transcripts capture sentiment, but not emotional arousal. Second, we compare strategies for the automated analysis of sentiment in text. We find that leading approaches performed reasonably well, but sentiment dictionaries generated using word embeddings surpassed these other approaches. Finally, we test the robustness of the approach based on word embeddings. Although the methodology is reasonably robust to alternative specifications, we find that dictionaries created using word embeddings are sensitive to the choice of seed words and to training corpus size. We conclude by discussing the implications for analyses of political speech.

摘要

自动情绪分析在政治学中被广泛使用。立法记录的数字化增加了已建立的工具在文本中自动分析情感的潜在应用。然而，与书面不同，在言语中表达情感涉及语调、面部表情和肢体语言。本文借鉴加拿大下议院的带注释文本和视频的新数据集，做了三件事。首先，我们检查文字记录是否捕捉到演讲的情感内容。我们发现成绩单能捕捉情绪，但不能捕捉情绪唤起。其次，我们比较了文本情感自动分析的策略。我们发现领先的方法表现得相当好，但使用词嵌入生成的情感词典超过了这些其他方法。最后，我们测试了基于词嵌入的方法的鲁棒性。尽管该方法对替代规范相当稳健，但我们发现使用词嵌入创建的词典对种子词的选择和训练语料库大小很敏感。最后，我们讨论了对政治言论分析的影响。