A new language is introduced for describing hypotheses about fluctuations of measurable properties in streams of timestamped data, and as prime example, we consider trends of emotions in the constantly flowing stream of Twitter messages. The language, called EmoEpisodes, has a precise semantics that measures how well a hypothesis characterizes a given time interval; the semantics is parameterized so it can be adjusted to different views of the data. EmoEpisodes is extended to a query language with variables standing for unknown topics and emotions, and the query-answering mechanism will return instantiations for topics and emotions as well as time intervals that provide the largest deflections in this measurement. Experiments are performed on a selection of Twitter data to demonstrates the usefulness of the approach.
|Title of host publication||Flexible Query Answering Systems : 10th International Conference, FQAS 2013, Granada, Spain, September 18-20, 2013. Proceedings|
|Place of Publication||Heidelberg|
|Publication status||Published - 2013|
|Series||Lecture notes in artificial intelligence|
Andreasen, T., Christiansen, H., & Have, C. T. (2013). Querying Sentiment Development over Time. In Flexible Query Answering Systems: 10th International Conference, FQAS 2013, Granada, Spain, September 18-20, 2013. Proceedings (Vol. 8132, pp. 613-624). Heidelberg: Springer. Lecture notes in artificial intelligence, Vol.. 8132