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A Type-Theoretic Account of Neg-Raising Predicates in Tree Adjoining Grammars Read chapter Read first chapter

2014 | OriginalPaper | Chapter

Argumentative Insights from an Opinion Classification Task on a French Corpus

Authors : Marc Vincent, Grégoire Winterstein

Published in: New Frontiers in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

This work deals with sentiment analysis on a corpus of French product reviews. We first introduce the corpus and how it was built. Then we present the results of two classification tasks that aimed at automatically detecting positive, negative and neutral reviews by using various machine learning techniques. We focus on methods that make use of feature selection techniques. This is done in order to facilitate the interpretation of the models produced so as to get some insights on the relative importance of linguistic items for marking sentiment and opinion. We develop this topic by looking at the output of the selection processes on various classes of lexical items and providing an explanation of the selection in argumentative terms.

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Footnotes
1
Some approaches use the presence of negation as a feature. This was experimented with, but it did not improve the results and it added a great number of features which slowed down the learning. Therefore it was abandoned.
 
2
We ignore the borderline case of car (\(\approx \) because/since).
 
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Metadata
Title
Argumentative Insights from an Opinion Classification Task on a French Corpus
Authors
Marc Vincent
Grégoire Winterstein
Copyright Year
2014
Publisher
Springer International Publishing
Book
New Frontiers in Artificial Intelligence

Print ISBN: 978-3-319-10060-9

Electronic ISBN: 978-3-319-10061-6

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-10061-6_9

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