Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
dc.contributor.author | Memiş, Erkut | |
dc.contributor.author | Akarkamçı (Kaya), Hilal | |
dc.contributor.author | Yeniad, Mustafa | |
dc.contributor.author | Rahebi, Javad | |
dc.contributor.author | López Guede, José Manuel ![]() | |
dc.date.accessioned | 2024-02-05T19:10:00Z | |
dc.date.available | 2024-02-05T19:10:00Z | |
dc.date.issued | 2024-01-10 | |
dc.identifier.citation | Applied Sciences 14(2) : (2024) // Article ID 588 | es_ES |
dc.identifier.issn | 2076-3417 | |
dc.identifier.uri | http://hdl.handle.net/10810/64670 | |
dc.description.abstract | Nowadays, Twitter is one of the most popular social networking services. People post messages called “tweets”, which may contain photos, videos, links and text. With the vast amount of interaction on Twitter, due to its popularity, analyzing Twitter data is of increasing importance. Tweets related to finance can be important indicators for decision makers if analyzed and interpreted in relation to stock market. Financial tweets containing keywords from the BIST100 index were collected and the tweets were tagged as “POSITIVE”, “NEGATIVE” and “NEUTRAL”. Binary and multi-class datasets were created. Word embedding and pre-trained word embedding were used for tweet representation. As classifiers, Neural Network, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and GRU-CNN models were used in this study. The best results for binary and multi-class datasets were observed with pre-trained word embedding with the CNN model (83.02%, 72.73%). When word embedding was employed, the Neural Network model had the best results on the multi-class dataset (63.85%) and GRU-CNN had the best results on the binary dataset (80.56%). | es_ES |
dc.description.sponsorship | The authors were supported by the Mobility Lab Foundation, a governmental organization of the Provincial Council of Araba and the local council of Vitoria-Gasteiz under the following project grant: “Utilización de drones en la movilidad de mercancías”. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/es/ | |
dc.subject | data mining | es_ES |
dc.subject | deep learning | es_ES |
dc.subject | sentiment classification | es_ES |
dc.subject | financial | es_ES |
dc.subject | tweet | es_ES |
dc.subject | Borsa Istanbul | es_ES |
dc.title | Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2024-01-26T14:11:00Z | |
dc.rights.holder | © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2076-3417/14/2/588 | es_ES |
dc.identifier.doi | 10.3390/app14020588 | |
dc.departamentoes | Ingeniería de sistemas y automática | |
dc.departamentoeu | Sistemen ingeniaritza eta automatika |
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Except where otherwise noted, this item's license is described as © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).