| DC Field | Value | Language |
|---|
| dc.contributor.author | Самаль, Д. И. | - |
| dc.contributor.author | Прытков, В. А. | - |
| dc.contributor.author | Трубчик, А. И. | - |
| dc.date.accessioned | 2016-10-25T10:50:26Z | - |
| dc.date.accessioned | 2017-07-18T11:52:25Z | - |
| dc.date.available | 2016-10-25T10:50:26Z | - |
| dc.date.available | 2017-07-18T11:52:25Z | - |
| dc.date.issued | 2016 | - |
| dc.identifier.citation | Трубчик, А. И. Прогнозирование событий с помощью ленты Twitter / А. И. Трубчик, Д. И. Самаль, В. А. Прытков // BIG DATA and Advanced Analytics. Использование BIG DATA для оптимизации бизнеса и информационных технологий : сборник материалов II международной научно-практической конференции, Минск, 15-17 июня 2016 г. / редкол. : М. П. Батура [и др.]. – Минск : БГУИР, 2016. – С. 325-331. | ru_RU |
| dc.identifier.isbn | 978-985-543-237-2 | - |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/9682 | - |
| dc.description.abstract | The paper explores the possibility of forecasting based on the intelligent analysis of a feed of the Twitter social network. The object of this analysis is the Bitcoin crypto currency market.
The theoretical basis for this forecasting approach is the efficient market hypothesis, which states that
new information can be used for gaining economic advantage. The source of this information is the
sentiment analysis of Twitter messages. The work proposes a new algorithm of determining the
emotions of Twitter messages with three possible grade levels: negative, neutral and positive. Thanks
to comprehensive correlation analysis and forecasting experiments, it was determined that there is a relation between the analyzed source data, and the prediction accuracy of Bitcoin market movements reached 63,27%. | ru_RU |
| dc.language.iso | ru | ru_RU |
| dc.publisher | БГУИР | ru_RU |
| dc.subject | материалы конференций | ru_RU |
| dc.title | Прогнозирование событий с помощью ленты Twitter | ru_RU |
| dc.type | Article | ru_RU |
| Appears in Collections: | BIG DATA and Advanced Analytics. Использование BIG DATA для оптимизации бизнеса и информационных технологий (2016)
|
