Financial Sentiment Lexicon Analysis

Abstract

The modern stock market is a popular place to increase wealth and generate income, but the fundamental problem of when to buy or sell shares, or which stocks to buy has not been solved. With the availability of the Internet and its financial social networks, such as StockTwits and SeekingAlpha, investors around the world have new opportunities to gather and share their experiences. Individual experts can predict the movement of the stock market in financial social networks with reasonable accuracy, but how accurate is a large group of such experts in aggregate? One way to answer this question is by examining the sentiment of a massive group of these authors towards various stocks. By extracting the sentiment of the whole group, a collective prediction can be observed. Although sentiment extraction is a major technical challenge, the lexicon-based approach is an effective method of determining how positive or negative the content of a text document is. In this paper, we investigate if we can improve the performance of sentiment extraction from financial social media data by using lexicon-based approaches.