A Fine-Grained Annotated Corpus for Target-Based Opinion Analysis of Economic and Financial Narratives - Archive ouverte HAL Access content directly
Conference Papers Year : 2021

A Fine-Grained Annotated Corpus for Target-Based Opinion Analysis of Economic and Financial Narratives

Abstract

In this paper about aspect-based sentiment analysis (ABSA), we present the first version of a fine-grained annotated corpus for targetbased opinion analysis (TBOA) to analyze economic activities or financial markets. We have annotated, at an intra-sentential level, a corpus of sentences extracted from documents representative of financial analysts' most-read materials by considering how financial actors communicate about the evolution of event trends and analyze related publications (news, official communications, etc.). Since we focus on identifying the expressions of opinions related to the economy and financial markets, we annotated the sentences that contain at least one subjective expression about a domain-specific term. Candidate sentences for annotations were randomly chosen from texts of specialized press and professional information channels over a period ranging from 1986 to 2021.
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Dates and versions

hal-04336550 , version 1 (11-12-2023)

Identifiers

  • HAL Id : hal-04336550 , version 1

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Patrick Paroubek, Jiahui Hu. A Fine-Grained Annotated Corpus for Target-Based Opinion Analysis of Economic and Financial Narratives. Third Workshop on Economics and Natural Language Processing, Association for Computational Linguistics, Nov 2021, Punta Cana, Dominican Republic. ⟨hal-04336550⟩
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