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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Bibliographic Details
Published in:Computational intelligence and neuroscience Vol. 2020; no. 2020; pp. 1 - 14
Main Authors: Salim, Naomie, Naeem, Muhammad, Zeb, Asim, Biswal, R. R., Zareei, Mahdi, Gul, Muhammad Adnan, Khan, Atif, Saeed, Yousaf
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
John Wiley & Sons, Inc
Subjects:
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
Spain
ISSN:1687-5265, 1687-5273, 1687-5273
Online Access:Get full text
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Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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Academic Editor: Luis Javier Herrera
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2020/7526580