# Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
## 2 Related Studies
The prediction of exchange rate movements using tweets has been studied by [PERSON] and [PERSON]. The keywords \"#USD/TR\", \"USD/TR\", \"Dollar\", \"#Dollar\" were used for tweet collection. Collected tweets' sentiments and the daily exchange rate of USD/TR were analyzed by them. They used value 1 for increasing exchange rate and 0 for the rest of the cases. They also categorized the collected tweets as Buy, Sell and Neutral. As a result, they found a remarkable relationship between the exchange rate and the sentiments of tweets [18]. [PERSON] and [PERSON] studied sentiment analysis methods on microblogging sites that use new user metrics. They proposed the measurement of the financial community's sentiment polarity on microblogging sites. In addition, they analyzed the correlation between the behavior of the Borsa Istanbul index and the mood of the financial community weekly using the Pearson correlation coefficient method [19]. [PERSON], [PERSON] and [PERSON] studied sentiment analysis and Twitter. They used both n-gram and lexicon methods, implementing two different models. They concluded that the lexicon method has a better performance than the n-gram method [20]. [PERSON], [PERSON] and [PERSON] studied stock market predictions using Twitter moods. The text content of daily tweets were analyzed by using two mood tracking tools, OpinionFinder and Google-Profile of Mood States (GPOMS). They used a Granger causality analysis and a self-organizing fuzzy Neural Network to explore their hypothesis that public mood states could be used to predict change in DJIA closing values. They found that using specific public mood dimensions remarkably improve DJIA predictions [21]. [PERSON], [PERSON] and [PERSON] studied \"sentiment analysis using learning approaches over emois for Turkish tweets\". They used bag-of-words and fastText representations for evaluated sentiment classification models, including sentiment analysis performed over emojis/emoticons. Their results show that there are no notable distinctions between these models [22]. [PERSON] et al. studied stream-based sentiment analysis in the financial domain. They explored the relationship between sentiments expressed in tweets related to selected companies and their stock prices movements. They used the SVM classifier for tweet categorization based on positive, negative and neutral statements. They found that there is a relationship between company-related tweets and their stock price changes, and that tweets could be used as a measure for stock price directions [23]. [PERSON] and [PERSON] studied \"sentiment analysis of tweets based on document vectors using supervised learning and semi-supervised learning\". They carried out sentiment analysis using Turkish and English tweets [24]. [PERSON], [PERSON] and [PERSON] studied sentiment analysis using machine learning and word embedding for Turkish tweets. They used SVM and Random Forest classifiers for sentiment classification. They also used vector embedding for Turkish tweet representation. Their results show that sectoral-based tweet classification gives better results than general or non-domain tweet classification [25]. A financial tweet refers to a message shared on the Twitter platform that delves into financial subjects, encompassing discussions on stock market trends, economic news, investment strategies, tips on personal finance and updates related to cryptocurrencies. Such tweets serve the purpose of disseminating information, offering commentary and initiating conversations among individuals with an interest in the field of finance [26]. Categories of financial tweets:  
Market updates: These tweets furnish current and immediate information regarding stock prices, market indices and economic indicators [21]. Analysts' perspectives: Financial analysts frequently convey their insights and predictions on Twitter, impacting investment decisions [27]. Personal finance guidance: Authorities, bloggers and individuals disseminate practical advice and strategies for effectively managing personal finances [28].