Work detail

Stock Market Prediction: A Multiclass Classification on Emotions and Sentiment Analysis for Tweets and News Headlines

Author: Mgr. Dejan Lazeski, B.Sc.
Year: 2020 - summer
Leaders: prof. Ing. Evžen Kočenda M.A., Ph.D., DSc.
Work type: Finance, Financial Markets and Banking
Language: English
Pages: 78
Awards and prizes:
Abstract: In this thesis, we look beyond extracting binary sentiment in regards to News
Headlines and Tweets. As a data source, we target tweets and headlines from
well-known financial newspapers, explicitly addressing the top 5 Big Tech companies. To examine the effectiveness of sentiment and Ekman’s emotions in
predicting future stock price movements, we develop multiclass emotion and
sentiment classifiers utilizing a supervised learning approach. Moreover, we
manually annotate our corpora for positive, negative, and neutral sentiment
as well as one of Ekman’s emotions: anger, joy, surprise, sadness. We did not
confirm any robust correlation between daily stock price movements and the
distribution of sentiment and emotions. However, we did observe that tweets
are less neutral than news headlines. Finally, we implement a simple investing strategy by extracting sentiment polarity scores using VADER and other
metrics such as followers and shares. Two classifiers, SVM and ANN, delivered
robust predictions for Google and Amazon compared to weak predictions for the
rest of the companies. Nevertheless, the results suggest that sentiment polarity
can effectively predict future stock price movements compared to finer-grained
emotion classification.




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