Movie Review Summarization Using Supervised Learning and Graph-Based Ranking Algorithm
With the growing information on web, online movie review is becoming a significant information resource for Internet users. However, online users post thousands of movie reviews on daily basis and it is hard for them to manually summarize the reviews. Movie review mining and summarization is one of...
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| Published in: | Computational intelligence and neuroscience Vol. 2020; no. 2020; pp. 1 - 14 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Cairo, Egypt
Hindawi Publishing Corporation
2020
Hindawi John Wiley & Sons, Inc |
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| ISSN: | 1687-5265, 1687-5273, 1687-5273 |
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| Abstract | With the growing information on web, online movie review is becoming a significant information resource for Internet users. However, online users post thousands of movie reviews on daily basis and it is hard for them to manually summarize the reviews. Movie review mining and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is desirable to summarize the lengthy movie reviews, and it will allow users to quickly recognize the positive and negative aspects of a movie. This study employs a feature extraction technique called bag of words (BoW) to extract features from movie reviews and represent the reviews as a vector space model or feature vector. The next phase uses Naïve Bayes machine learning algorithm to classify the movie reviews (represented as feature vector) into positive and negative. Next, an undirected weighted graph is constructed from the pairwise semantic similarities between classified review sentences in such a way that the graph nodes represent review sentences, while the edges of graph indicate semantic similarity weight. The weighted graph-based ranking algorithm (WGRA) is applied to compute the rank score for each review sentence in the graph. Finally, the top ranked sentences (graph nodes) are chosen based on highest rank scores to produce the extractive summary. Experimental results reveal that the proposed approach is superior to other state-of-the-art approaches. |
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| AbstractList | With the growing information on web, online movie review is becoming a significant information resource for Internet users. However, online users post thousands of movie reviews on daily basis and it is hard for them to manually summarize the reviews. Movie review mining and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is desirable to summarize the lengthy movie reviews, and it will allow users to quickly recognize the positive and negative aspects of a movie. This study employs a feature extraction technique called bag of words (BoW) to extract features from movie reviews and represent the reviews as a vector space model or feature vector. The next phase uses Naïve Bayes machine learning algorithm to classify the movie reviews (represented as feature vector) into positive and negative. Next, an undirected weighted graph is constructed from the pairwise semantic similarities between classified review sentences in such a way that the graph nodes represent review sentences, while the edges of graph indicate semantic similarity weight. The weighted graph-based ranking algorithm (WGRA) is applied to compute the rank score for each review sentence in the graph. Finally, the top ranked sentences (graph nodes) are chosen based on highest rank scores to produce the extractive summary. Experimental results reveal that the proposed approach is superior to other state-of-the-art approaches.With the growing information on web, online movie review is becoming a significant information resource for Internet users. However, online users post thousands of movie reviews on daily basis and it is hard for them to manually summarize the reviews. Movie review mining and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is desirable to summarize the lengthy movie reviews, and it will allow users to quickly recognize the positive and negative aspects of a movie. This study employs a feature extraction technique called bag of words (BoW) to extract features from movie reviews and represent the reviews as a vector space model or feature vector. The next phase uses Naïve Bayes machine learning algorithm to classify the movie reviews (represented as feature vector) into positive and negative. Next, an undirected weighted graph is constructed from the pairwise semantic similarities between classified review sentences in such a way that the graph nodes represent review sentences, while the edges of graph indicate semantic similarity weight. The weighted graph-based ranking algorithm (WGRA) is applied to compute the rank score for each review sentence in the graph. Finally, the top ranked sentences (graph nodes) are chosen based on highest rank scores to produce the extractive summary. Experimental results reveal that the proposed approach is superior to other state-of-the-art approaches. With the growing information on web, online movie review is becoming a significant information resource for Internet users. However, online users post thousands of movie reviews on daily basis and it is hard for them to manually summarize the reviews. Movie review mining and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is desirable to summarize the lengthy movie reviews, and it will allow users to quickly recognize the positive and negative aspects of a movie. This study employs a feature extraction technique called bag of words (BoW) to extract features from movie reviews and represent the reviews as a vector space model or feature vector. The next phase uses Naïve Bayes machine learning algorithm to classify the movie reviews (represented as feature vector) into positive and negative. Next, an undirected weighted graph is constructed from the pairwise semantic similarities between classified review sentences in such a way that the graph nodes represent review sentences, while the edges of graph indicate semantic similarity weight. The weighted graph-based ranking algorithm (WGRA) is applied to compute the rank score for each review sentence in the graph. Finally, the top ranked sentences (graph nodes) are chosen based on highest rank scores to produce the extractive summary. Experimental results reveal that the proposed approach is superior to other state-of-the-art approaches. |
| Audience | Academic |
| Author | Biswal, R. R. Zareei, Mahdi Saeed, Yousaf Gul, Muhammad Adnan Khan, Atif Salim, Naomie Naeem, Muhammad Zeb, Asim |
| AuthorAffiliation | 4 Department of Information Technology, University of Haripur, Haripur, KP, Pakistan 3 Department of Computer Science, Abbottabad University of Science and Technology, Abbottabad 25000, Pakistan 1 Department of Computer Science, Islamia College University Peshawar, Peshawar 25000, KP, Pakistan 2 Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Zapopan, Jalisco 45138, Mexico 5 School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia |
| AuthorAffiliation_xml | – name: 4 Department of Information Technology, University of Haripur, Haripur, KP, Pakistan – name: 2 Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Zapopan, Jalisco 45138, Mexico – name: 1 Department of Computer Science, Islamia College University Peshawar, Peshawar 25000, KP, Pakistan – name: 3 Department of Computer Science, Abbottabad University of Science and Technology, Abbottabad 25000, Pakistan – name: 5 School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia |
| Author_xml | – sequence: 1 fullname: Salim, Naomie – sequence: 2 fullname: Naeem, Muhammad – sequence: 3 fullname: Zeb, Asim – sequence: 4 fullname: Biswal, R. R. – sequence: 5 fullname: Zareei, Mahdi – sequence: 6 fullname: Gul, Muhammad Adnan – sequence: 7 fullname: Khan, Atif – sequence: 8 fullname: Saeed, Yousaf |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32565772$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1155_2020_5812715 crossref_primary_10_1186_s40537_022_00680_6 crossref_primary_10_1109_ACCESS_2021_3090219 crossref_primary_10_3390_electronics13030544 crossref_primary_10_1007_s11042_024_18296_8 crossref_primary_10_1007_s13369_024_09540_2 crossref_primary_10_1155_2021_7871490 crossref_primary_10_1155_2022_7132226 crossref_primary_10_1109_ACCESS_2023_3283461 crossref_primary_10_1007_s12652_022_03748_6 crossref_primary_10_1051_e3sconf_202018903019 crossref_primary_10_1007_s11042_023_16358_x |
| Cites_doi | 10.1016/s0169-7552(98)00110-x 10.1016/j.ipm.2010.11.003 10.1109/jstsp.2012.2229690 10.1016/j.ipm.2015.06.002 10.1613/jair.1523 10.1109/tasl.2012.2217129 10.1109/tkde.2015.2405553 10.1561/1500000011 10.1145/505282.505283 10.1016/j.chb.2013.05.024 10.1016/j.ipm.2014.02.001 10.1155/2019/2537689 10.1016/j.eswa.2016.05.001 10.1016/j.eswa.2014.04.004 10.1017/S1351324909005129 10.1016/j.ipm.2015.02.001 10.1007/s11518-009-5100-7 10.1007/s10115-007-0114-2 10.1007/s12652-012-0143-x 10.1016/j.eswa.2012.12.084 10.2478/cait-2012-0011 10.4304/jetwi.2.3.258-268 10.1007/s10618-011-0238-6 10.1023/a:1009930203452 10.1007/s10115-009-0194-2 10.1109/tsmcc.2011.2136334 10.1108/eb046814 10.1177/0165551507077406 10.1016/j.knosys.2016.01.030 10.1016/j.csl.2013.04.001 10.1007/s10462-016-9475-9 10.1016/j.eswa.2019.03.045 10.1016/j.ipm.2016.12.002 |
| ContentType | Journal Article |
| Copyright | Copyright © 2020 Atif Khan et al. COPYRIGHT 2020 John Wiley & Sons, Inc. Copyright © 2020 Atif Khan et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0 Copyright © 2020 Atif Khan et al. 2020 |
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| SubjectTerms | Algorithms Bayesian analysis Classification Computational linguistics Customer feedback Customers Data mining Dictionaries Feature extraction Graph theory Humans Information overload Information resources Internet Language Language processing Learning algorithms Machine learning Motion pictures Motion Pictures - statistics & numerical data Movie reviews Natural language interfaces Natural Language Processing Nodes Product reviews Ranking Rankings Reviews Semantics Sentences Sentiment analysis Supervised learning Supervised Machine Learning Support vector machines User statistics Websites |
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| Title | Movie Review Summarization Using Supervised Learning and Graph-Based Ranking Algorithm |
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