# Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
## 2 Related Studies  
The sentiment analysis of tweets related to finance can be a significant indicator for investors when analyzed and interpreted according to the stock market. Automatically determining tweets' sentiments is an attractive research area for many researchers. Feature vectors for text representation, classification techniques such as SVN, CNN, LSTM, Naive Bayes, etc., and relations between tweets and stock markets are just a few research areas in this field. Although many sentiment analysis studies have been conducted on Twitter data, there are not enough studies on these subjects in the Turkish language and on Turkish stock markets. [PERSON] and [PERSON] have studied sentiment extraction for specific subjects from a document, instead of document classification [13]. Also the review of \"sentiment analysis\" has been reviewed in reference [14]. [PERSON] has studied sentiment analysis on English tweets in order to predict S&P 500 index movement. He used data mining to draw out the companies affecting the S&P 500 index, in order to rank these companies and to determine patterns. In his thesis, he showed that classifier ensembles perform better than classic classifiers in the process of classifying tweets; his prediction model has an accuracy rate above 80% [15]. The relationship between the stock market index and Turkish tweets was studied by [PERSON] and [PERSON]. They used 113 words and eight classes for their emotion corpus. When these words were found in tweets, they count them and calculated average happiness values. They showed that the relationship between the stock market and tweet data is approximately 45% [16]. The relationship between social media and daily stock prices was investigated by [PERSON] and [PERSON]. A telecommunication company from Borsa Istanbul was selected. \begin{table}
\begin{tabular}{c c} \hline \hline
**Aspect** & **Description** \\ \hline Language Emphasis & Enhances sentiment analysis capabilities in Turkish, a language with limited linguistic resources, thereby broadening applicability across diverse cultural contexts. \\ \hline Financial Domain & Specializes in Turkish financial tweets, offering valuable perspectives into market sentiment and investor behaviors within this specific domain. \\ \hline Multiple Deep Learning Models & Evaluates and contrasts five distinct deep learning architectures tailored for Turkish financial tweets, delivering comprehensive insights into their respective performances. \\ \hline Utilization of Pre-trained Embeddings & Investigates and illustrates the efficacy of utilizing pre-trained word embedding from fastText for sentiment analysis in Turkish, contributing to the optimization of the analytical process. \\ \hline \hline \end{tabular}
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Table 1: Originality and novelty. \begin{table}
\begin{tabular}{c c} \hline \hline
**Aspect** & **Description** \\ \hline Financial Market Sentiment Analysis & Assists traders, analysts and portfolio managers in making informed decisions by evaluating investor sentiment. \\ \hline Risk Identification and Management & Pinpoints potential risks and opportunities in specific stocks or sectors, aiding in the formulation and execution of effective risk management strategies. \\ \hline Enhanced Investor Communication & Facilitates personalized communication with investors based on social media feedback, thereby enhancing engagement and overall satisfaction. \\ \hline Informed Market Research & Offers valuable insights into public perceptions of financial products, services and regulations, serving as a foundation for market research and informed product development initiatives. \\ \hline \hline \end{tabular}
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Table 2: Practical Implications. For a given period, daily data (opening price, closing price etc.) was collected. Sentiment analysis was applied for the same period. According the Spearman's rank correlation test results, a negative and moderate correlation exist between the daily stock price and public sentiments in tweets [17].