More than Bags of Words: Sentiment Analysis with Word Embeddings
More than Bags of Words: Sentiment Analysis with Word Embeddings
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DOI：
10.1080/19312458.2018.1455817
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发表时间：
2018-04
影响因子：
11.4
通讯作者：
Elena Rudkowsky;Martin Haselmayer;Matthias Wastian;Marcelo Jenny;Stefan Emrich;M. Sedlmair
中科院分区：
文献类型：
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作者：
Elena Rudkowsky;Martin Haselmayer;Matthias Wastian;Marcelo Jenny;Stefan Emrich;M. Sedlmair
摘要：超越主流的词袋方法进行情感分析，我们引入了一种基于分布式词嵌入的替代过程。词嵌入的优势在于捕捉词义相似性的能力。我们使用词嵌入作为监督机器学习程序的一部分，该程序估计议会演讲中的消极程度。该程序的准确性通过众包训练句子进行评估；通过对奥地利议会演讲中消极情绪模式的研究来了解其外部有效性。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。
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.