A system of systems service design for social media analytics

Most social media analyses such as sentiment analysis for microblogs are often built as standalone, endpoint to endpoint applications. This makes the collaboration among distributed software and data service providers to create composite social analytic solutions difficult. This paper first proposes a system of systems service architecture (SoS-SA) design for social media analytics that support and facilitate efficient collaboration among distributed service providers. Then we propose a novel Twitters sentiment analysis service implemented on top of this design to illustrate its potentials. Current sentiment classification applications based on supervised learning methods relies too heavily on the chosen large training datasets, approaches using automatically generated training datasets also often result in the huge imbalance between the subjective classes and the objective classes in the sentiment of tweets, making it difficult to obtain good recall performance for the subjective ones. To address this issue, our proposed solution is based on a semi-supervised learning method for tweet sentiment classification. Experiments show that the performance of our method is better than those of the previous work.