Exploiting textual and relationship information for fine-grained financial sentiment analysis

Sentiment analysis aims to identify the way in which sentiments are expressed in texts. State-of-the-art approaches base their analyses solely on the given text, which complicates the detection of implicit sentiments and ignores the role of sentiment contagion. In the financial domain, sentiment could be spread across multiple platforms, such as in company and analyst reports, news articles, and microblogs. Thus, to capture implicit sentiments and the contagion process, we introduce a novel approach that leverages the text and contextual information of a record for fine-grained sentiment analysis. Based on this information, we generate a record representation, which is used in an adapted feed-forward neural network. Our proposed solution improves the performance by as much as 15% and 234% relative to multiple baselines. Our work demonstrates the impact of implicit sentiment as well as the importance of different relationships for sentiment prediction on company and analyst reports, news articles, and microblogs. For example, we identified timestamp information as being non-essential for the fine-grained sentiment analysis of company and analyst reports. Although we are able to showcase improvements in financial sentiment analysis, sentiment contagion and limited context are two common problems that continue to prevail. Therefore, by re-defining sentiment analysis as a multi-text problem, our proposed solution can be applied across multiple domains and text types, such as product reviews. (C) 2021 The Author(s). Published by Elsevier B.V.

Automated summary

Sentiment analysis aims to identify how emotions are expressed in texts, especially in the financial domain where emotions can spread across multiple platforms. This research introduces a novel approach leveraging text and contextual information for fine-grained sentiment analysis, improving performance significantly.