Contextualizing Emerging Trends in Financial News Articles

TL;DR

This paper introduces a new approach for detecting emerging trends in lengthy financial news articles, specifically about Microsoft during COVID-19, using topic modeling and keyword similarity analysis, and compares results with Google Trends.

Contribution

It presents a novel dataset, a baseline method called Contextual Leap2Trend, and evaluates trend detection in financial news against Google Trends, addressing challenges of formal text analysis.

Findings

The dataset is publicly accessible.

The baseline method effectively captures trend dynamics.

COVID-19 influenced Microsoft-related trends significantly.

Abstract

Identifying and exploring emerging trends in the news is becoming more essential than ever with many changes occurring worldwide due to the global health crises. However, most of the recent research has focused mainly on detecting trends in social media, thus, benefiting from social features (e.g. likes and retweets on Twitter) which helped the task as they can be used to measure the engagement and diffusion rate of content. Yet, formal text data, unlike short social media posts, comes with a longer, less restricted writing format, and thus, more challenging. In this paper, we focus our study on emerging trends detection in financial news articles about Microsoft, collected before and during the start of the COVID-19 pandemic (July 2019 to July 2020). We make the dataset accessible and propose a strong baseline (Contextual Leap2Trend) for exploring the dynamics of similarities between pairs of keywords based on topic modelling and term frequency. Finally, we evaluate against a gold standard (Google Trends) and present noteworthy real-world scenarios regarding the influence of the pandemic on Microsoft.