A Sentiment and Emotion Annotated Dataset for Bitcoin Price Forecasting Based on Reddit Posts - The first knowledge sharing application in Vietnam

@inproceedings{seroyizhko-etal-2022-sentiment,
    title = "A Sentiment and Emotion Annotated Dataset for Bitcoin Price Forecasting Based on {R}eddit Posts",
    author = "Seroyizhko, Pavlo  and
      Zhexenova, Zhanel  and
      Shafiq, Muhammad Zohaib  and
      Merizzi, Fabio  and
      Galassi, Andrea  and
      Ruggeri, Federico",
    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-1.27",
    pages = "203--210",
    abstract = "Cryptocurrencies have gained enormous momentum in finance and are nowadays commonly adopted as a medium of exchange for online payments. After recent events during which GameStop{'}s stocks were believed to be influenced by WallStreetBets subReddit, Reddit has become a very hot topic on the cryptocurrency market. The influence of public opinions on cryptocurrency price trends has inspired researchers on exploring solutions that integrate such information in crypto price change forecasting. A popular integration technique regards representing social media opinions via sentiment features. However, this research direction is still in its infancy, where a limited number of publicly available datasets with sentiment annotations exists. We propose a novel Bitcoin Reddit Sentiment Dataset, a ready-to-use dataset annotated with state-of-the-art sentiment and emotion recognition. The dataset contains pre-processed Reddit posts and comments about Bitcoin from several domain-related subReddits along with Bitcoin{'}s financial data. We evaluate several widely adopted neural architectures for crypto price change forecasting. Our results show controversial benefits of sentiment and emotion features advocating for more sophisticated social media integration techniques. We make our dataset publicly available for research.",
}

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    <abstract>Cryptocurrencies have gained enormous momentum in finance and are nowadays commonly adopted as a medium of exchange for online payments. After recent events during which GameStop’s stocks were believed to be influenced by WallStreetBets subReddit, Reddit has become a very hot topic on the cryptocurrency market. The influence of public opinions on cryptocurrency price trends has inspired researchers on exploring solutions that integrate such information in crypto price change forecasting. A popular integration technique regards representing social media opinions via sentiment features. However, this research direction is still in its infancy, where a limited number of publicly available datasets with sentiment annotations exists. We propose a novel Bitcoin Reddit Sentiment Dataset, a ready-to-use dataset annotated with state-of-the-art sentiment and emotion recognition. The dataset contains pre-processed Reddit posts and comments about Bitcoin from several domain-related subReddits along with Bitcoin’s financial data. We evaluate several widely adopted neural architectures for crypto price change forecasting. Our results show controversial benefits of sentiment and emotion features advocating for more sophisticated social media integration techniques. We make our dataset publicly available for research.</abstract>
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%0 Conference Proceedings
%T A Sentiment and Emotion Annotated Dataset for Bitcoin Price Forecasting Based on Reddit Posts
%A Seroyizhko, Pavlo
%A Zhexenova, Zhanel
%A Shafiq, Muhammad Zohaib
%A Merizzi, Fabio
%A Galassi, Andrea
%A Ruggeri, Federico
%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 seroyizhko-etal-2022-sentiment
%X Cryptocurrencies have gained enormous momentum in finance and are nowadays commonly adopted as a medium of exchange for online payments. After recent events during which GameStop’s stocks were believed to be influenced by WallStreetBets subReddit, Reddit has become a very hot topic on the cryptocurrency market. The influence of public opinions on cryptocurrency price trends has inspired researchers on exploring solutions that integrate such information in crypto price change forecasting. A popular integration technique regards representing social media opinions via sentiment features. However, this research direction is still in its infancy, where a limited number of publicly available datasets with sentiment annotations exists. We propose a novel Bitcoin Reddit Sentiment Dataset, a ready-to-use dataset annotated with state-of-the-art sentiment and emotion recognition. The dataset contains pre-processed Reddit posts and comments about Bitcoin from several domain-related subReddits along with Bitcoin’s financial data. We evaluate several widely adopted neural architectures for crypto price change forecasting. Our results show controversial benefits of sentiment and emotion features advocating for more sophisticated social media integration techniques. We make our dataset publicly available for research.
%U https://aclanthology.org/2022.finnlp-1.27
%P 203-210

Mục lục bài viết

Markdown (Informal)

[A Sentiment and Emotion Annotated Dataset for Bitcoin Price Forecasting Based on Reddit Posts](https://aclanthology.org/2022.finnlp-1.27) (Seroyizhko et al., FinNLP 2022)

A Sentiment and Emotion Annotated Dataset for Bitcoin Price Forecasting Based on Reddit Posts (Seroyizhko et al., FinNLP 2022)

ACL

Pavlo Seroyizhko, Zhanel Zhexenova, Muhammad Zohaib Shafiq, Fabio Merizzi, Andrea Galassi, and Federico Ruggeri. 2022. A Sentiment and Emotion Annotated Dataset for Bitcoin Price Forecasting Based on Reddit Posts. In Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP), pages 203–210, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.