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

Author

Gangemi, Aldo ; Presutti, Valentina ; Reforgiato Recupero, Diego

Author_Institution

LIPN, Univ. Paris 13, Paris, France

Volume

9

Issue

1

fYear

2014

fDate

Feb. 2014

Firstpage

20

Lastpage

30

Abstract

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%).

Keywords

cognition; data mining; graph theory; knowledge representation; natural language processing; semantic Web; FRED; KR components; NLP; Sentilo; cognitively-inspired frames; frame-based detection; heuristic graph mining approach; knowledge representation components; machine reader; natural language processing; neo-Davidsonian assumption; opinion holders; opinion sentences; semantic Web; sentiment analysis; subtopic detection; topic detection; Computational modeling; Data mining; Feature extraction; Knowledge representation; Natural language processing; OWL; Semantics; Sentiment analysis; Syntactics;

fLanguage

English

Journal_Title

Computational Intelligence Magazine, IEEE

Publisher

ieee

ISSN

1556-603X

Type

jour

DOI

10.1109/MCI.2013.2291688

Filename

6710244