# Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
## 1 Introduction  
Nowadays, the most popular data sharing field is social media; therefore, social media sites accumulate huge amounts of data. Twitter [1] is a commonly used social media data sources. Twitter has nearly 700 million users and 58 million tweets on average per day [2]. People post messages that are called as tweets, which may include texts, videos, links, etc. Because of its huge popularity and usage, analyzing tweets posted by users has become more and more important. Therefore, automatically detecting tweets' sentiments is an attractive research area for many researchers.  
Sentiment analysis is the outcome of people's emotions, attitudes, opinions, sentiments, etc., in their sharing's, which can be written or spoken. This concept especially focuses on polarity detection [3], which identifies negative and positive opinions in the text.  
Sentiment analysis is carried out at three levels: the word or phrase level, the sentence level and the document level [4]. Generally, lexicon-based, learning-based and hybrid-based approaches [5] are used to realize sentiment classification problems. Figure 1 shows different sentiment analysis approaches and algorithms.  
Tweets are, in a way, microblogs or short texts, so our sentiment analysis is performed at the sentence level. In our work, we used learning-based approaches to define sentiments in sentences. Financial news from tweets and the sentiment analysis of these tweets may contain important information or indicators for the financial or stock market. Althoughmany studies have been conducted in English in the field of sentiment analysis and financial sentiment analysis, not many studies have been published in Turkish yet. Turkish financial tweets were collected with determined keywords from the BIST 100 index using association rule mining [6] and the tweets were tagged as \"POSITIVE\", \"NEGATIVE\" and \"NEUTRAL\". Binary datasets including only positive and negative classes, and multi-class dataset including positive, negative and neutral classes were created.  
Noisy or unclear sentences negatively affected the sentiment classification process. In order to prepare these tweets for analysis, we used pre-processing, which included stop word removal, normalization processes, etc. The \"ITU Turkish NLP Web Service API\" was utilized for the Turkish text normalization process [7].  
Deep learning algorithms and methods have provided great improvements in the fields of pattern recognition and image recognition. These improvements led to Natural Language Processing (NLP) researchers to be able to focus on deep learning methods. The use of dense vector representations based on Neural Networks has achieved better results for NLP tasks. The success of word embedding [8; 9] and deep learning methods [10] caused the trend of using deep learning algorithms in NLP tasks. In contrast to the traditional machine-learning-based NLP systems, which use handmade features, deep learning enables automatic feature representation learning. Handmade features have several bottlenecks [11]. We used word embedding and pre-trained word embedding with fastText [12] for feature representation in our work.  
Neural Network, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and GRU-CNN models were used as sentiment classifiers in this study. The performances of these models were evaluated based on their accuracy.  
The arrangement of this paper is as follows: the introduction is included in Section 1; works related to sentiment analysis and Turkish financial tweet data are discussed in Section 2; Section 3 contains descriptions of the materials and methods used in our work; the results are presented in Section 4; and Section 5 includes our conclusions. The key highlights  
Figure 1: Sentiment analysis approaches and algorithms [5].  
have been concisely outlined in Tables 1 and 2, successfully differentiating the groundbreaking contributions of this research from its practical applications for stakeholders within the financial industry.