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Publication
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| Title of Article |
Topic Modeling on Crowd Trading Ideas for Digital Asset Price Prediction |
| Date of Acceptance |
5 January 2023 |
| Journal |
| Title of Journal |
International Journal of Applied Engineering & Technology |
| Standard |
SCOPUS |
| Institute of Journal |
Roman Science Publications |
| ISBN/ISSN |
ISSN: 2633-4828 |
| Volume |
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| Issue |
5 |
| Month |
January |
| Year of Publication |
2023 |
| Page |
6-12 |
| Abstract |
Tradingview is the most widely recognized
social network platform for stock and digital asset
trading, which a number of investors access to share
their thoughts on investments each day. The present
study sought to analyze contents published in the “Ideas”
forum on Tradingview from November 2021 – October
2022 using topic modeling through Latent Dirichlet
Allocation (LDA). The results demonstrated that 9,553
texts drawn from Tradingview’s Ideas were classified
into six topics about digital asset trading. Based on the
hypothesis and the prediction, it was found that the most
shared idea, particularly 3,354 texts, concerning trading
strategy and risk/profit, followed by 3,101 about trend
line, high/low price pattern, and support/resistance. The
number of texts on both topics accounted for 67.56%. It
can be implied that investments in digital assets require
good strategies, risk management, analysis of price
trends, and knowledge of support and resistance.
Identifying significant words and topics using LDA
uncovers a larger number of words latent in the texts
than hypothesiz. |
| Keyword |
digital assets, crowd idea user, topic modeling; Latent Dirichlet Allocation, digital trading, trading ideas |
| Author |
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| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ตีพิมพ์แล้ว |
| Level of Publication |
นานาชาติ |
| citation |
false |
| Part of thesis |
true |
| Attach file |
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| Citation |
0
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Journal Publication
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