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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7288188/ https://www.ncbi.nlm.nih.gov/pubmed/32565772 http://dx.doi.org/10.1155/2020/7526580 |
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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 |
author_sort | Khan, Atif |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7288188 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-72881882020-06-20 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 Comput Intell Neurosci Research Article 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 2020-06-02 /pmc/articles/PMC7288188/ /pubmed/32565772 http://dx.doi.org/10.1155/2020/7526580 Text en Copyright © 2020 Atif Khan et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article 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 |
title | 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 |
title_full_unstemmed | 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_sort | movie review summarization using supervised learning and graph-based ranking algorithm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7288188/ https://www.ncbi.nlm.nih.gov/pubmed/32565772 http://dx.doi.org/10.1155/2020/7526580 |
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