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Published in: The Journal of Supercomputing 3/2017

13-07-2016

AspectFrameNet: a frameNet extension for analysis of sentiments around product aspects

Authors: Sanjay Chatterji, Nitish Varshney, Ranjan Kumar Rahul

Published in: The Journal of Supercomputing | Issue 3/2017

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Abstract

In the real-world scenarios, customer tries to evaluate a product based on sentiments conveyed by its users or reviewers. AspectFrameNet provides a framework that helps the semantic analysis of text inputs from social feeds and news (Voice of Customer) by disambiguating the contexts in which the lexical units are used. To this end, we have used this framework in analysing sentiments around different aspects of internet of things. We have tested this framework for 31 interrelated aspects in mobile domain and three possible sentiments (positive, negative and neutral).

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Appendix
Available only for authorised users
Footnotes
1
The Stanford tools and guidelines are downloadable from: http://​nlp.​stanford.​edu/​software/​.
 
2
The ARK-Tweet-NLP tools of CMU are downloadable from: http://​www.​ark.​cs.​cmu.​edu/​TweetNLP/​.
 
3
CRF++: Yet Another CRF toolkit Version 0.58 has been downloaded from: http://​crfpp.​googlecode.​com/​svn/​trunk/​doc/​index.​html?​source=​navbar#download.
 
4
LIBSVM Version 3.20 has been downloaded from: http://​www.​csie.​ntu.​edu.​tw/​~cjlin/​libsvm/​.
 
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Metadata
Title
AspectFrameNet: a frameNet extension for analysis of sentiments around product aspects
Authors
Sanjay Chatterji
Nitish Varshney
Ranjan Kumar Rahul
Publication date
13-07-2016
Publisher
Springer US
Published in
The Journal of Supercomputing / Issue 3/2017
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-016-1808-6

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