Stock Prediction by Polyglot Sentiment Analysis on Twitter

Abstract

Research in the economics field has extensively documented the impact of media sentiments on the stock market. Sentiment analysis, as a tool to predict equity prices, has been popularized in the past years. Recently, Twitter has received a lot of attention due to the diversity of opinions on social media platforms. A common obstruction to sentiment analysis is the resource gap between English and other languages. This pilot study examines the effect of polyglotism in tweets concerning bilingual companies and develops a model that extracts sentiments from polyglot tweets to make price predictions. Results suggest that taking non-English tweets into consideration decreases errors in price predictions and that the random forest models have higher performance than linear regression models. The results of this pilot study need to be confirmed with larger sets of data.