@inproceedings{nguyen-etal-2022-contextualizing,
title = "Contextualizing Emerging Trends in Financial News Articles",
author = {Nguyen, Nhu Khoa and
Delahaut, Thierry and
Boros, Emanuela and
Doucet, Antoine and
Lejeune, Ga{\"e}l},
editor = "Chen, Chung-Chi and
Huang, Hen-Hsen and
Takamura, Hiroya and
Chen, Hsin-Hsi",
booktitle = "Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.finnlp-1.1",
doi = "10.18653/v1/2022.finnlp-1.1",
pages = "1--9",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Contextualizing Emerging Trends in Financial News Articles
%A Nguyen, Nhu Khoa
%A Delahaut, Thierry
%A Boros, Emanuela
%A Doucet, Antoine
%A Lejeune, Gaël
%Y Chen, Chung-Chi
%Y Huang, Hen-Hsen
%Y Takamura, Hiroya
%Y Chen, Hsin-Hsi
%S Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F nguyen-etal-2022-contextualizing
%X 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.
%R 10.18653/v1/2022.finnlp-1.1
%U https://aclanthology.org/2022.finnlp-1.1
%U https://doi.org/10.18653/v1/2022.finnlp-1.1
%P 1-9
Markdown (Informal)
[Contextualizing Emerging Trends in Financial News Articles](https://aclanthology.org/2022.finnlp-1.1) (Nguyen et al., FinNLP 2022)
ACL
- Nhu Khoa Nguyen, Thierry Delahaut, Emanuela Boros, Antoine Doucet, and Gaël Lejeune. 2022. Contextualizing Emerging Trends in Financial News Articles. In Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP), pages 1–9, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.