Borsa tahminlemede metin analitiği

Trying to predict the future using social media data and analytics is very popular today. With this motivation, we aimed to make stock market predictions by creating different analysis models for 10 different banks traded in "Borsa Istanbul 100" over three different groups that we selected on social media. The groups determined within the scope of the study can be detailed as tweets posted by banks from their accounts, tweets posted with the name of the bank, and tweets with the name of the bank posted from approved accounts. In our analysis, we used various variations, including the tweets' sentiments, replies, retweets, and like counts of the tweets, the effects of daily currency (Dollar, Euro, and Gold) prices, and the changes in stock changes up to 3 days. We applied some pre-processing techniques to the collected data and defined sentiment classes for sentiment analysis, created six different models, and analyzed them using 7 different classification algorithms such as Multi-Layer Perceptron, Random Forest, and deep learning algorithm. We labeled our dataset with 3 different classes to predict the stock market prices of the selected data group. According to these classes, the stock price can be positive, negative, and neutral. After all the models and analysis, we got a total of 1440 different results. According to our results, the accuracy rates vary according to the data groups and models we have chosen. The tweet group in which the name of the banks is mentioned can be shown as the most successful data group and we can easily say that there is a certain relation between social media and stock market prices.