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.Our annotation scheme relies on various linguistic markers like domain-specific vocabulary, syntactic structures, and rhetorical relations to explicitly describe the author's subjective stance. We investigated and evaluated the recourse to automatic pre-annotation with existing natural language processing technologies to alleviate the annotation workload. Our aim is to propose a corpus usable on the one hand as training material for the automatic detection of the opinions expressed on an extensive range of domain-specific aspects and on the other hand as a gold standard for evaluation TBOA.In this paper, we present our pre-annotation models and evaluations of their performance, introduce our annotation scheme and report on the main characteristics of our corpus.

EconBERTa: Towards Robust Extraction of Named Entities in Economics

“…Previously released datasets typically comprise news articles or press releases annotated for sentiment analysis (Malo et al, 2014), event extraction (Jacobs and Hoste, 2022;Lee et al, 2022;Han et al, 2022), opinion analysis (Hu and Paroubek, 2021) or causality detection in finance (Mariko et al, 2020). In addition to news articles, corporate reports (Loukas et al, 2021;Händschke et al, 2018) are text corpora from economics and business, but lack token-level annotations.…”

Computational Understanding of Narratives: A Survey

“…Narratives can therefore be used as strategic tools, to shape local events and promote collective opinions [25], [50]. Narratives can also be further divided into domain-narratives, stories that form within specific fields of study, such as finance [56], the gaming industry [21], and clinical sciences [124]. More discussion on domain-narratives is available in Section IV-A.…”