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Frame-Based Detection of Opinion Holders and Topics: A Model and a Tool

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3 Author(s)
Gangemi, A. ; LIPN, Univ. Paris 13, Paris, France ; Presutti, V. ; Reforgiato Recupero, D.

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%).

Published in:

Computational Intelligence Magazine, IEEE  (Volume:9 ,  Issue: 1 )