基于文本情感计算、聚合和预测的R包计量学

David Ardia, Keven Bluteau, S. Borms, Kris Boudt
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引用次数: 19

摘要

我们提供了一个使用R包sentometrics优化文本情感索引的实践介绍。文本情感分析越来越多地用于挖掘文本数据的潜在信息价值。sentometrics包实现了一个直观的框架,可以有效地计算大量文本的情感分数,将分数聚合到多个时间序列中,并使用这些时间序列来预测其他变量。该软件包的工作流程通过内置的来自两家美国主要期刊的新闻文章语料库来说明,以预测CBOE波动率指数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The R Package sentometrics to Compute, Aggregate and Predict with Textual Sentiment
We provide a hands-on introduction to optimized textual sentiment indexation using the R package sentometrics. Textual sentiment analysis is increasingly used to unlock the potential information value of textual data. The sentometrics package implements an intuitive framework to efficiently compute sentiment scores of numerous texts, to aggregate the scores into multiple time series, and to use these time series to predict other variables. The workflow of the package is illustrated with a built-in corpus of news articles from two major U.S. journals to forecast the CBOE Volatility Index.
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