KTH Matematik  

Matematisk Statistik

Tid: 16 november 2015 kl 10.15-11.00.

Seminarierummet 3418, Institutionen för matematik, KTH, Lindstedtsvägen 25, plan 4. Karta!

Föredragshållare: Anton Osika

Titel: Statistical analysis of online linguistic sentiment data with financial applications (Master's thesis)

Abstract Gavagai is a company that uses different methods to aggregate sentiment towards specific topics from a large stream of real time published documents. Gavagai wants to find a procedure to decide which \textit{way of measuring sentiment} (sentiment measure) towards a topic is most useful in a given context. This work discusses what criterion are desirable for aggregating sentiment and derives and evaluates procedures to select "optimal" sentiment measures. Three novel models for selecting a set of sentiment measures that describe independent attributes of the aggregated data are evaluated. The models can be summarized as: maximizing variance of the last principal component of the data, maximizing the differential entropy of the data and, in the special case of selecting an additional sentiment measure, maximizing the unexplained variance conditional on the previous sentiment measures. When exogenous time varying data considering a topic is available, the data can be used to select the sentiment measure that best explain the data. With this goal in mind, the hypothesis that sentiment data can be used to predict financial volatility and political poll data is tested. The null hypothesis can not be rejected. A framework for aggregating sentiment measures in a mathematically coherent way is summarized in a road map.

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Sidansvarig: Filip Lindskog
Uppdaterad: 25/02-2009