Sentilo is a model and a tool to detect holders and topics of opinion sentences. Sentilo implements an approach based on the neo-Davidsonian assumption that events and situations are the primary entities for contextualizing opinions, which makes it able to distinguish holders, main topics, and sub-topics of an opinion. It uses a heuristic graph mining approach that relies on FRED, a machine reader for the Semantic Web that leverages Natural Language Processing (NLP) and Knowledge Representation (KR) components jointly with cognitively-inspired frames. The evaluation results are excellent for holder detection (F1: 95%), very good for subtopic detection (F1: 78%), and good for topic detection (F1: 68%). © 2014 IEEE.
Frame-based detection of opinion holders and topics: A model and a tool
Institute of Electrical and Electronics Engineers,, New York , Stati Uniti d'America
IEEE computational intelligence magazine 9 (2014): 20–30. doi:10.1109/MCI.2013.2291688
info:cnr-pdr/source/autori:Gangemi A.; Presutti V.; Reforgiato Recupero D./titolo:Frame-based detection of opinion holders and topics: A model and a tool/doi:10.1109/MCI.2013.2291688/rivista:IEEE computational intelligence magazine/anno:2014/pagina_da:20/