Movie Review Summarization Using Supervised Learning and Graph-Based Ranking Algorithm

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.

HAS: Hybrid Analysis of Sentiments for the perspective of customer review summarization

“…This section presents the review of some recent work carried out in the domain of SA (Wang et al 2020;Hao et al 2020;Zhu et al 2021;Naresh and Krishna 2021;Singh et al 2021;Munuswamy et al 2021;Ayyub et al 2020;Oyebode et al 2020;Iqbal et al 2019;Khan et al 2020). Wang et al (2020) proposed the SentiDiff algorithm for Twitter data SA.…”

“…Iqbal et al ( 2019 ) proposed a Genetic Algorithm (GA) based feature reduction mechanism to bridge the gap between the machine learning and lexicon-based techniques for improving scalability and accuracy. The simple mechanism for SA is proposed by Khan et al ( 2020 ) using the Bag of Words (BoW) feature extraction and Naïve Bayes (NB) classifier. The proposed model classifies the movie reviews either as positive or negative.…”

“…From the above studies, we observed that SA-based methods (Wang et al 2020 ; Hao et al 2020 ; Zhu et al 2021 ; Naresh and Krishna 2021 ; Singh et al 2021 ; Munuswamy et al 2021 ; Ayyub et al 2020 ; Oyebode et al 2020 ; Iqbal et al 2019 ; Khan et al 2020 ) are not sufficient to address the challenges related to the exact representation of feelings, emotions, and opinions from online reviews. For that purpose, the ABSA techniques (Schouten et al 2018 ; Alqaryouti et al 2019 ; Wang 2021 ; Kumar et al 2020 ; Nandal et al 2020 ; Li et al 2020 ; Prathi et al 2020 ; Alamanda 2020 ; Shams et al 2020 ; Bie and Yang 2021 ) that have been proposed recently are studied but they also have limited scope or limited investigations.…”

A Comprehensive Survey of Abstractive Text Summarization Based on Deep Learning

“…As a result, information overload is becoming more and more serious. Almost every day, users must spend a lot of time browsing all kinds of cumbersome texts and ltering out redundant information, which dramatically reduces their e ciency [1][2][3][4][5][6][7][8][9][10][11]. erefore, how to quickly locate the information needed from the text resources, then summarize and compress it, has become an urgent and fundamental problem to be solved.…”

Corrigendum to “Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics”

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In the article titled "Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics" [1], a citation was missing to the authors' recent related work [2]. At the request of the authors, the article has been corrected inline to reduce the similarity and include a discussion of how the methods compare.

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