Marcus Hassler is the founder and CTO of econob (www.econob.com), a start-up company located in Austria. He finished his PhD in Applied Computer Science at the Alpen-Adria University in Klagenfurt 2009 with focus on natural language processing and mathematics. Currently he is responsible for project and product development of cloud-based Big Data applications that process large amounts of natural language texts. His competence covers customized linguistic services, multi-language processing, text semantics, textual quantification, mood detection and high-level sentiment analysis.
Classification and Estimation of Finance Relevant Topics in Social Media
Social Media channels provide a valuable and rich source of business information relevant to Fintechs, especially the capital management. After integration and management of the high data loads, the challenge is real-time evaluation of that information regarding specific insights and impacts. Through solving that difficulty a wide range of opportunities for Fintechs including research, trading or asset allocation arise. With TWIction Finance (http://twiction.lingrep.com) econob presents a platform that enables domain-dependent live Twitter monitoring of statements relevant to the capital management. In this talk the focus is on measuring and combining the mood in the capital management domain with concrete evaluation and classification of topics like “tax”, “fraud”, “buy/sell”, “trading”, and “global economy”. A key factor to mood detection within small and often contextless textual fragments is a highly specialized linguistic assessment by the identification of simple and complex linguistic patterns. For capital management detecting the mood and opinion is only half of the bill, because in the field of Big Data the noise distorts the accuracy so that a multi-dimensional analysis by a combination of classification and concepts becomes essential. As a result the financial expert is capable to make opinions on issues relevant to finance on millions of tweets by the combination of concepts, mood and classification e.g. VW is downgraded from hold to sell. This presentation provides the building blocks that enable this relevance filtered and concept related sentiment assignments of financial classification. Further, application areas and future directions of this kind of tools are depicted.