QMOS: Query-based multi-documents opinion-oriented summarization

Sentiment analysis concerns the study of opinions expressed in a text. This paper presents the QMOS method, which employs a combination of sentiment analysis and summarization approaches. It is a lexicon-based method to query-based multi-documents summarization of opinion expressed in reviews. QMOS...

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Main Authors: Abdi, A., Shamsuddin, S. M., Aliguliyev, R. M.
Format: Article
Language:English
Published: Elsevier Ltd. 2018
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Online Access:http://eprints.utm.my/id/eprint/81850/1/AsadAbdi2018_QMOSQueryBasedMultiDocuments.pdf
http://eprints.utm.my/id/eprint/81850/
http://dx.doi.org/10.1016/j.ipm.2017.12.002
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spelling my.utm.818502019-09-29T08:13:59Z http://eprints.utm.my/id/eprint/81850/ QMOS: Query-based multi-documents opinion-oriented summarization Abdi, A. Shamsuddin, S. M. Aliguliyev, R. M. QA75 Electronic computers. Computer science Sentiment analysis concerns the study of opinions expressed in a text. This paper presents the QMOS method, which employs a combination of sentiment analysis and summarization approaches. It is a lexicon-based method to query-based multi-documents summarization of opinion expressed in reviews. QMOS combines multiple sentiment dictionaries to improve word coverage limit of the individual lexicon. A major problem for a dictionary-based approach is the semantic gap between the prior polarity of a word presented by a lexicon and the word polarity in a specific context. This is due to the fact that, the polarity of a word depends on the context in which it is being used. Furthermore, the type of a sentence can also affect the performance of a sentiment analysis approach. Therefore, to tackle the aforementioned challenges, QMOS integrates multiple strategies to adjust word prior sentiment orientation while also considers the type of sentence. QMOS also employs the Semantic Sentiment Approach to determine the sentiment score of a word if it is not included in a sentiment lexicon. On the other hand, the most of the existing methods fail to distinguish the meaning of a review sentence and user's query when both of them share the similar bag-of-words; hence there is often a conflict between the extracted opinionated sentences and users’ needs. However, the summarization phase of QMOS is able to avoid extracting a review sentence whose similarity with the user's query is high but whose meaning is different. The method also employs the greedy algorithm and query expansion approach to reduce redundancy and bridge the lexical gaps for similar contexts that are expressed using different wording, respectively. Our experiment shows that the QMOS method can significantly improve the performance and make QMOS comparable to other existing methods. Elsevier Ltd. 2018 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/81850/1/AsadAbdi2018_QMOSQueryBasedMultiDocuments.pdf Abdi, A. and Shamsuddin, S. M. and Aliguliyev, R. M. (2018) QMOS: Query-based multi-documents opinion-oriented summarization. Information Processing and Management, 54 (2). pp. 318-338. ISSN 0306-4573 http://dx.doi.org/10.1016/j.ipm.2017.12.002 DOI:10.1016/j.ipm.2017.12.002
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Abdi, A.
Shamsuddin, S. M.
Aliguliyev, R. M.
QMOS: Query-based multi-documents opinion-oriented summarization
description Sentiment analysis concerns the study of opinions expressed in a text. This paper presents the QMOS method, which employs a combination of sentiment analysis and summarization approaches. It is a lexicon-based method to query-based multi-documents summarization of opinion expressed in reviews. QMOS combines multiple sentiment dictionaries to improve word coverage limit of the individual lexicon. A major problem for a dictionary-based approach is the semantic gap between the prior polarity of a word presented by a lexicon and the word polarity in a specific context. This is due to the fact that, the polarity of a word depends on the context in which it is being used. Furthermore, the type of a sentence can also affect the performance of a sentiment analysis approach. Therefore, to tackle the aforementioned challenges, QMOS integrates multiple strategies to adjust word prior sentiment orientation while also considers the type of sentence. QMOS also employs the Semantic Sentiment Approach to determine the sentiment score of a word if it is not included in a sentiment lexicon. On the other hand, the most of the existing methods fail to distinguish the meaning of a review sentence and user's query when both of them share the similar bag-of-words; hence there is often a conflict between the extracted opinionated sentences and users’ needs. However, the summarization phase of QMOS is able to avoid extracting a review sentence whose similarity with the user's query is high but whose meaning is different. The method also employs the greedy algorithm and query expansion approach to reduce redundancy and bridge the lexical gaps for similar contexts that are expressed using different wording, respectively. Our experiment shows that the QMOS method can significantly improve the performance and make QMOS comparable to other existing methods.
format Article
author Abdi, A.
Shamsuddin, S. M.
Aliguliyev, R. M.
author_facet Abdi, A.
Shamsuddin, S. M.
Aliguliyev, R. M.
author_sort Abdi, A.
title QMOS: Query-based multi-documents opinion-oriented summarization
title_short QMOS: Query-based multi-documents opinion-oriented summarization
title_full QMOS: Query-based multi-documents opinion-oriented summarization
title_fullStr QMOS: Query-based multi-documents opinion-oriented summarization
title_full_unstemmed QMOS: Query-based multi-documents opinion-oriented summarization
title_sort qmos: query-based multi-documents opinion-oriented summarization
publisher Elsevier Ltd.
publishDate 2018
url http://eprints.utm.my/id/eprint/81850/1/AsadAbdi2018_QMOSQueryBasedMultiDocuments.pdf
http://eprints.utm.my/id/eprint/81850/
http://dx.doi.org/10.1016/j.ipm.2017.12.002
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score 13.19449