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2015 | OriginalPaper | Chapter

Supervised Opinion Frames Detection with RAID

Authors : Alessio Palmero Aprosio, Francesco Corcoglioniti, Mauro Dragoni, Marco Rospocher

Published in: Semantic Web Evaluation Challenges

Publisher: Springer International Publishing

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Abstract

Most systems for opinion analysis focus on the classification of opinion polarities and rarely consider the task of identifying the different elements and relations forming an opinion frame. In this paper, we present RAID, a tool featuring a processing pipeline for the extraction of opinion frames from text with their opinion expressions, holders, targets and polarities. RAID leverages a lexical, syntactic and semantic analysis of text, using several NLP tools such as dependency parsing, semantic role labelling, named entity recognition and word sense disambiguation. In addition, linguistic resources such as SenticNet and the MPQA Subjectivity Lexicon are used both to locate opinions in the text and to classify their polarities according to a fuzzy model that combines the sentiment values of different opinion words. RAID was evaluated on three different datasets and is released as open source software under the GPLv3 license.

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previous chapter Detecting Sentiment Polarities with Sentilo
Footnotes
4
In literature, terms defining roles in opinions may vary: in particular, the holder can also be expressed as source, and the target as topic.
 
16
This normalization does not affect the expression returned by the system and is required as expressions extracted in Sect. 3.2 might not be aligned with parse tree constituents.
 
19
The choice of a supervised approach in place of hard-coded rules is motivated also by observing that none of the datasets considered in Sect. 2.2 provides clear guidelines for marking holders and targets, resulting in heterogeneous and sometimes inconsistent annotations.
 
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Metadata
Title
Supervised Opinion Frames Detection with RAID
Authors
Alessio Palmero Aprosio
Francesco Corcoglioniti
Mauro Dragoni
Marco Rospocher
Copyright Year
2015
Book
Semantic Web Evaluation Challenges

Print ISBN: 978-3-319-25517-0

Electronic ISBN: 978-3-319-25518-7

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-25518-7_22

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