Available at: http://digitalcommons.calpoly.edu/theses/723
Date of Award
MS in Computer Science
Every week there are over a billion new posts to Twitter services and many of those messages contain feedback to companies about their services. One company that has recognized this unused source of information is Netflix. That is why Netflix initiated the development of a system that will let them respond to the millions of Twitter and Netflix users that are acting as sensors and reporting all types of user visible outages. This system will enhance the feedback loop between Netflix and its customers by increasing the amount of customer feedback that is being received by Netflix and reducing the time it takes for Netflix to receive the reports and respond to them. The goal of the SPOONS (Swift Perceptions of Online Negative Situations) system is to use Twitter posts to determine when Netflix users are reporting a problem with any of the Netflix services. This work covers a subset of the meth- ods implemented in the SPOONS system. The volume methods detect outages through time series analysis of the volume of a subset of the tweets that contain the word “netflix”. The sentiment methods first process the tweets and extract a sentiment rating which is then used to create a time series. Both time series are monitored for significant increases in volume or negative sentiment which indicates that there is currently an outage in a Netflix service. This work contributes: the implementation and evaluation of 8 outage detection methods; 256 sentiment estimation procedures and an evaluation of each; and evaluations and discussions of the real time applicability of the system. It also provides explanations for each aspect of the implementation, evaluations, and conclusions so future companies and researchers will be able to more quickly create detection systems that are applicable to their specific needs.