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

Author:

Memiş Erkut1,Akarkamçı (Kaya) Hilal2,Yeniad Mustafa1,Rahebi Javad3ORCID,Lopez-Guede Jose Manuel4ORCID

Affiliation:

1. Department of Computer Engineering, Ankara Yıldırım Beyazıt University, Ankara 06010, Türkiye

2. Turkish Embassy Office of Educational Counsellor, 1062 Budapest, Hungary

3. Department of Software Engineering, Istanbul Topkapi University, Istanbul 34087, Türkiye

4. Department of Automatic Control and System Engineering, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain

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%).

Funder

Mobility Lab Foundation

Publisher

MDPI AG

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