Financial Text Classification using Machine Learning Techniques

The motivation behind this project is to be able to make a sentiment analysis on Turkish financial documents. Classify-ing financial news as negative or positive helps predicting the change in stock price for a particular company. In our work, we process more than 20,000 documents obtained from financial institutions in Turkey. We translate Loughran & McDonald's word list into Turkish and label the news based on the number of negative words they contain. We then construct a Logistic Regression model with Ll penalization using bag-of-words ap-proach to predict the labels of news. We achieve an Fl score of 0.88 for negative documents by doing sentiment analysis using the negative financial word list. We also observe that most of the negative financial news are labeled correctly.