ABSTRACT

Moving beyond the dominant bag-of-words approach to sentiment analysis we introduce an alternative procedure based on distributed word embeddings. The strength of word embeddings is the ability to capture similarities in word meaning. We use word embeddings as part of a supervised machine learning procedure which estimates levels of negativity in parliamentary speeches. The procedure’s accuracy is evaluated with crowdcoded training sentences; its external validity through a study of patterns of negativity in Austrian parliamentary speeches. The results show the potential of the word embeddings approach for sentiment analysis in the social sciences.

中文翻译：

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胜过一揽子单词：带有单词嵌入的情感分析

摘要

除了使用占主导地位的词袋方法进行情感分析外，我们还介绍了一种基于分布式词嵌入的替代过程。词嵌入的优势在于能够捕获词义的相似性。我们使用词嵌入作为有监督的机器学习程序的一部分，该程序估计议会演讲中否定性的程度。使用人群编码的训练语句评估过程的准确性；通过对奥地利议会演讲中否定性模式的研究，了解其外部有效性。结果显示了词嵌入方法在社会科学中进行情感分析的潜力。