Data By the Bay is the first Data Grid conference matrix with 6 vertical application areas spanned by multiple horizontal data pipelines, platforms, and algorithms. We are unifying data science and data engineering, showing what really works to run businesses at scale.
In this talk we will discuss the evolution of the news analytics landscape from the perspective of the participants in the global financial industry. We will discuss the development route of several current key ML/NLP projects at Bloomberg, such as sentiment analysis of financial news, prediction of market impact, novelty detection, social media monitoring, question answering and topic clustering. These interdisciplinary problems lie at the intersection of linguistics, finance, computer science and mathematics, requiring methods from signal processing, machine vision and other fields. We will talk about the methods, problem formulation, and throughout, talk about practicalities of delivering machine learning solutions to problems of finance, highlighting issues such as importance of appropriate problem decomposition, validation and interpretability. We will also summarize the current state of the art and discuss possible future directions for the applications of natural language processing methods in finance. The talk will end with a Q&A session.