TY - GEN
T1 - Querying Sentiment Development over Time
AU - Andreasen, Troels
AU - Christiansen, Henning
AU - Have, Christian Theil
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
M3 - Article in proceedings
SN - 978-3-642-40768-0
VL - 8132
T3 - Lecture notes in artificial intelligence
SP - 613
EP - 624
BT - Flexible Query Answering Systems
PB - Springer
CY - Heidelberg
ER -