Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods

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%).

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Eser Adı
(dc.title)
Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
Yazar
(dc.contributor.author)
Cevat Rahebi
Yayın Yılı
(dc.date.issued)
2024
Tür
(dc.type)
Makale
Özet
(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%).
Açık Erişim Tarihi
(dc.date.available)
2024-01-10
Yayıncı
(dc.publisher)
Applied Sciences
Dil
(dc.language.iso)
En
Konu Başlıkları
(dc.subject)
Data mining
Konu Başlıkları
(dc.subject)
Deep learning
Konu Başlıkları
(dc.subject)
Sentiment classification
Konu Başlıkları
(dc.subject)
Financial
Konu Başlıkları
(dc.subject)
Tweet
Konu Başlıkları
(dc.subject)
Borsa Istanbul
Tek Biçim Adres
(dc.identifier.uri)
https://hdl.handle.net/20.500.14081/1921
Dergi
(dc.relation.journal)
Applied Sciences
Dergi Sayısı
(dc.identifier.issue)
2
Esere Katkı Sağlayan
(dc.contributor.other)
Rahebi, Javad
Esere Katkı Sağlayan
(dc.contributor.other)
Lopez-Guede, Jose Manuel
Esere Katkı Sağlayan
(dc.contributor.other)
Yeniad, Mustafa
Esere Katkı Sağlayan
(dc.contributor.other)
Akarkamci (Kaya), Hilal
Esere Katkı Sağlayan
(dc.contributor.other)
Memis, Erkut
DOI
(dc.identifier.doi)
10.3390/app14020588
Orcid
(dc.identifier.orcid)
0000-0001-9875-4860
Dergi Cilt
(dc.identifier.volume)
14
wosquality
(dc.identifier.wosquality)
Q2
wosauthorid
(dc.contributor.wosauthorid)
DNF-7937-2022
Department
(dc.contributor.department)
Yazılım Mühendisliği
Wos No
(dc.identifier.wos)
WOS:001149209900001
Veritabanları
(dc.source.platform)
Wos
Veritabanları
(dc.source.platform)
Scopus
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