计算机科学
自然语言处理
否定
编码
人工智能
构造(python库)
情绪分析
背景(考古学)
人工神经网络
古生物学
生物化学
化学
生物
基因
程序设计语言
作者
Sean Cao,Yongtae Kim,Angie Wang,Houping Xiao
出处
期刊:Social Science Research Network
[Social Science Electronic Publishing]
日期:2021-01-01
被引量:4
摘要
When quantifying qualitative information from unstructured textual data, traditional bag-of-words approaches capture only semantic features of single words/phrases. The context, the sequence of words, and the relations among words (i.e., higher-order interaction features) are ignored. We introduce deep neural networks (NNs) to encode and mimic human intelligence in processing natural language. Using the NN-based artificial intelligence, we construct a new sentiment measure that is specific to performance discussions and is adjusted for complex contextual negations. We find that this performance-specific sentiment explains cross-sectional returns and future operating performance better than umbrella sentiment proxies used in the literature.
科研通智能强力驱动
Strongly Powered by AbleSci AI