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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Hindawi Limited
2020
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Online Access: | http://eprints.utm.my/id/eprint/92557/1/NaomieSalim2020_MovieReviewSummarizationUsingSupervised.pdf http://eprints.utm.my/id/eprint/92557/ http://dx.doi.org/10.1155/2020/7526580 |
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my.utm.925572021-09-30T15:15:05Z http://eprints.utm.my/id/eprint/92557/ Movie review summarization using supervised learning and graph-based ranking algorithm Khan, Atif Gul, Muhammad Adnan Zareei, Mahdi Biswal, R. R. Zeb, Asim Naeem, Muhammad Saeed, Yousaf Salim, Naomie QA75 Electronic computers. Computer science 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. Hindawi Limited 2020-06 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/92557/1/NaomieSalim2020_MovieReviewSummarizationUsingSupervised.pdf Khan, Atif and Gul, Muhammad Adnan and Zareei, Mahdi and Biswal, R. R. and Zeb, Asim and Naeem, Muhammad and Saeed, Yousaf and Salim, Naomie (2020) Movie review summarization using supervised learning and graph-based ranking algorithm. Computational Intelligence and Neuroscience, 2020 . pp. 1-14. ISSN 1687-5265 http://dx.doi.org/10.1155/2020/7526580 DOI:10.1155/2020/7526580 |
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QA75 Electronic computers. Computer science Khan, Atif Gul, Muhammad Adnan Zareei, Mahdi Biswal, R. R. Zeb, Asim Naeem, Muhammad Saeed, Yousaf Salim, Naomie Movie review summarization using supervised learning and graph-based ranking algorithm |
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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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Article |
author |
Khan, Atif Gul, Muhammad Adnan Zareei, Mahdi Biswal, R. R. Zeb, Asim Naeem, Muhammad Saeed, Yousaf Salim, Naomie |
author_facet |
Khan, Atif Gul, Muhammad Adnan Zareei, Mahdi Biswal, R. R. Zeb, Asim Naeem, Muhammad Saeed, Yousaf Salim, Naomie |
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Khan, Atif |
title |
Movie review summarization using supervised learning and graph-based ranking algorithm |
title_short |
Movie review summarization using supervised learning and graph-based ranking algorithm |
title_full |
Movie review summarization using supervised learning and graph-based ranking algorithm |
title_fullStr |
Movie review summarization using supervised learning and graph-based ranking algorithm |
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Movie review summarization using supervised learning and graph-based ranking algorithm |
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movie review summarization using supervised learning and graph-based ranking algorithm |
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Hindawi Limited |
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2020 |
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http://eprints.utm.my/id/eprint/92557/1/NaomieSalim2020_MovieReviewSummarizationUsingSupervised.pdf http://eprints.utm.my/id/eprint/92557/ http://dx.doi.org/10.1155/2020/7526580 |
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13.18916 |