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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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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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 |
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QA75 Electronic computers. Computer science Abdi, A. Shamsuddin, S. M. Aliguliyev, R. M. QMOS: Query-based multi-documents opinion-oriented summarization |
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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. |
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Article |
author |
Abdi, A. Shamsuddin, S. M. Aliguliyev, R. M. |
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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 |
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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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1651866370932998144 |
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