Sentiment Classification of Cryptocurrency-Related Social Media Posts

被引：7

作者：

Kulakowski, Mikolaj ^{[1
]}

Frasincar, Flavius ^{[2
]}

机构：

[1] Erasmus Univ, Erasmus Sch Econ, NL-3062 PA Rotterdam, Netherlands

[2] Erasmus Univ, NL-3062 PA Rotterdam, Netherlands

来源：

IEEE INTELLIGENT SYSTEMS | 2023年 / 38卷 / 04期

关键词：

Training; Sentiment analysis; Social networking (online); Predictive models; Transformers; Cryptocurrency; Finance; Encoding; Natural language processing; Classification algorithms; Investment; Bidirectional control;

D O I：

10.1109/MIS.2023.3283170

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Many researchers agree that sentiment analysis can improve the performance of quantitative trading models. We develop two off-the-shelf solutions for analyzing the sentiments of cryptocurrency-related social media posts. First, we posttrain and fine-tune a Twitter-oriented model based on the bidirectional encoder representations from transformers (BERT) architecture, BERTweet, on the cryptocurrency domain, resulting in CryptoBERT. Second, we generate the language-universal cryptocurrency emoji (LUKE) sentiment lexicon and prediction pipeline, utilizing the sentiment of emojis prevalent in social media. CryptoBERT is highly accurate, while LUKE is suitable for non-English posts, thus allowing for direct classification and noisy label generation in less popular languages. Our research can help cryptocurrency investors develop trading software supported by sentiments mined from social media.

引用

页码：5 / 9

页数：5

共 10 条

[1] Araci D, 2019, Arxiv, DOI arXiv:1908.10063
[2] Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification
Chen, Zhenpeng
Shen, Sheng
Hu, Ziniu
Lu, Xuan
Mei, Qiaozhu
Liu, Xuanzhe
[J]. WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), 2019, : 251 - 262
[3] Choudhary N., 2018, INT C COMPUTATIONAL, P129, DOI 10.1007/978-3-031-23804-8_11
[4] Nguyen DQ, 2020, PROCEEDINGS OF THE 2020 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING: SYSTEM DEMONSTRATIONS, P9
[5] Devlin J, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P4171
[6] Hogenboom A, 2015, J WEB ENG, V14, P22
[7] Hutto C., 2014, P INT AAAI C WEB SOC, V8, P216, DOI [10.1609/icwsm.v8i1.14550, DOI 10.1609/ICWSM.V8I1.14550]
[8] Liu YH, 2019, Arxiv, DOI [arXiv:1907.11692, DOI 10.48550/ARXIV.1907.11692]
[9] Sennrich R, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1715
[10] Natural language based financial forecasting: a survey
Xing, Frank Z.
Cambria, Erik
Welsch, Roy E.
[J]. ARTIFICIAL INTELLIGENCE REVIEW, 2018, 50 (01) : 49 - 73

← 1 →