A Comparison of Neural Network Methods for Accurate Sentiment Analysis of Stock Market Tweets

Narges Tabari, Armin Seyeditabari, Tanya Peddi, Mirsad Hadzikadic, Wlodek Zadrozny

Abstract: Sentiment analysis of Twitter messages is a challenging task because they contain limited contextual information. Despite the popularity and significance of this task for financial institutions, models being used still lack high accuracy. Also, most of these models are not built specifically on stock market data. Therefore, there is still a need for a highly accurate model of sentiment classification that is specifically tuned and trained for stock market data.