Contextualizing Emerging Trends in Financial News Articles (2022)

Abstract: Identifying and exploring emerging trends in news is becoming more essential than ever with many changes occurring around the world 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 freely available and we also propose a strong baseline (Contextual Leap2Trend) for exploring the dynamics of similarities between pairs of keywords based on topic modeling 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.

## introduction digital news, through many means of diffusion (online publishing platforms, social media, blogs, etc.) is considerably influential, as it not only shapes and forms public opinion but can also be a factor in the decision-making process of many industries that uses technology to improve activities and performance. therefore, discovering hidden themes and trends residing in news data is essential to improve analyzing and managing development directions for many companies. the importance of identifying new trends before they emerge is further emphasized with the world-changing surrounding the health crisis triggered by the covid-19 pandemic. emerging trend detection is the task of automatically extracting topics that are gaining attention and on the verge of being trending @xcite . emerging topics usually indicate contents that are more popular in a short period, while growing in interest and utility over time. moreover, topics that become a trend can either be short-lived or last for a long time depending on the nature of the event (e.g., traffic accidents, natural disasters, election campaigns, regulation enforcement, etc.). based on the platform of publication, the data used for detecting emerging trends can be classified into two classes: social media text and formal text. corpora crawled from social media usually contain text that is short, concise and usually include social features (e.g. likes and retweets in twitter) which benefits the task as they can be used to measure the engagement and diffusion rate of content. because of this fact, data from social media has been extensively studied in various research @xcite . however, formal text data, unlike the short sub-300 characters social media posts, comes with longer, less restricted writing format, yet does not include any social features (e.g., news articles, official documents, reports). because of these differences, emerging trend detection on such data is rather under-researched. while there exist studies on financial data @xcite and news articles @xcite , most of them are based on techniques such as latent dirichlet allocation (lda) topic modelling @xcite , term frequency-inverse document frequency (tf-idf) weighting technique @xcite , or different clustering types @xcite for the identification of either "hot" topics or emerging themes. therefore, in this paper, our main contributions of our study are: (1) we build a dataset from the bloomberg's event driven feeds (edf), containing news about microsoft in the time interval from july 2019 to july 2020, which is the time period from six-month before and six-month after the covid-19 outbreak. (2) we combine term tf-idf and lda for a more precise generation of keywords (bi-grams). (3) we utilize the latest contextual embeddings to represent the real temporality and variation of the semantics during different periods. (4) we make the dataset accessible along with the snapshot of google trend data used in our evaluation 1 . the article is organized as follows. section 2 discusses related work on emerging theme detection. section 3 describes the data and explains our approach for detecting emerging trends in financialbased data and the experimental setup is established in section 4 alongside with the evaluation metrics and the detailed analysis regarding the impact of the pandemic on microsoft. lastly, section 5 concludes the article with remarks and points to future work. ## related work trend detection in formal datasets the general direction for trend detection in formal text data is to use statistical methods @xcite , topic modeling @xcite , and clustering @xcite using financial business patents, the research by @xcite aimed to identify emerging technology trends by applying latent dirichlet allocation (lda) with an exponentially weighted moving average of lda probability, which affects whether a topic is "hot" or "cold". a refined version of tf-idf, proposed by @xcite , aims to discover "hot" topics according to "hot" terms based on time distribution information, user attention, and k-means clustering. unlike previous work that tackled trend detection in official documents and newspapers, others focused on proposing new approaches using research and scientific papers, documents that generally contain citations and bibliographies that could be considered as additional features @xcite @xcite . however, exploiting bibliographies can have disadvantages in timeliness and content analysis, as discussed by @xcite . the authors further pro-posed an approach, called leap2trend using temporal word embedding that was generated by being trained on the data in an initial period of time, and then fine-tuned in the upcoming time frames. this approach also tracked the similarities between pairs of keywords over time, which yielded results suggesting the robustness and timeliness characteristics of the leap2trend. detecting trends in covid-19 pandemic recent works have also been conducted within the period of the covid-19 pandemic to study emerging trends within certain communities and gauge the impact of the outbreak through social media text @xcite . santis et al. (2020) employed term-frequency analysis, calculate nutrition and energy metrics, while also using social features in order to extract hot terms and build a topic graph through co-occurrence analysis using twitter data in italy. another research targeted peer-review papers regarding the covid-19 virus and apply word embeddings and machine learning models to track novel insight surrounding the spreading of the virus @xcite . ## methodology for the current study, we built a dataset by extracting a portion of the event-driven feeds (edf) data, a proprietary dataset provided by bloomberg foot_1 . bloomberg l.p. provides financial software tools and enterprise applications such as analytics and equity trading platform, data services, and news to financial companies and organizations. the edf data is massive and contains more than ten years of news collection that bloomberg published in multiple languages from 2010 up until the present. ## experimental setup we present the experiments on previously mentioned dataset from bloomberg news about microsoft from july 2019 to july 2020. this includes the description of the evaluation process, the results of our proposed system for emerging trend detection by comparing the shift in similarity ranking of pairs of keywords, and the elaboration of the story behind some keyword pairs that were in trend, and we deemed as interesting. ## conclusions this research addressed the drawback of existing methods in keyword extraction, bi-gram representation due to the differences in writing style between scientific papers and news articles. we, instead, introduced a combination of tf-idf and lda for generating potential keywords, and utilized contextual embeddings for the change in temporality. our contextual leap2trend system showed considerable improvements compared to the original method in some scenarios in length of prediction. moreover, we also presented several examples of emerging trends found in our data and the result also suggested that the approach has a good timeliness characteristic. in future work, we plan to introduce a better variety of data, such as news articles covering more companies and sector, to further experiment and improve our system. moreover, increasing the time intervals could also uphold the consideration to assess trends longevity.