Rome, May 3-4, 2018


Using MATLAB for Sentiment Analysis and Text Analytics

Sentiment scores, derived from text, such as newsfeeds and social media, offer important information to determine portfolio positions and trading signals, while text analytics can offer opportunities to identify misconduct. However, a document's sentiment is often a weak signal surrounded by a large amount of noise. Extracting that signal requires a variety of techniques for working with data both in text and numeric formats, as well as machine learning techniques for automating the sentiment scoring process on large quantities of data. Learn how to use text analytics capabilities in MATLAB® to build your own sentiment analysis tools. This presentation covers the entire sentiment scoring workflow, including importing social media feed data into MATLAB, preprocessing and cleaning up the raw text, converting text to a numeric format, and applying machine learning techniques to derive sentiment scores.