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Detection of Temporality at Discourse Level on Financial News by Combining Natural Language Processing and Machine Learning
April 3, 2024, 4:42 a.m. | Silvia Garc\'ia-M\'endez, Francisco de Arriba-P\'erez, Ana Barros-Vila, Francisco J. Gonz\'alez-Casta\~no
cs.LG updates on arXiv.org arxiv.org
Abstract: Finance-related news such as Bloomberg News, CNN Business and Forbes are valuable sources of real data for market screening systems. In news, an expert shares opinions beyond plain technical analyses that include context such as political, sociological and cultural factors. In the same text, the expert often discusses the performance of different assets. Some key statements are mere descriptions of past events while others are predictions. Therefore, understanding the temporality of the key statements in …
abstract arxiv beyond bloomberg business cnn context cs.ce cs.cl cs.ir cs.lg data detection discourse expert finance financial forbes language language processing machine machine learning market natural natural language natural language processing opinions political processing q-fin.st real data screening shares systems technical type
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