International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Subha Jyoti Das1, Riki Murakami1, Basabi Chakraborty2*

1 Graduate School of Software and Information Science, Iwate Prefectural University, Iwate, Japan
2 Faculty of Software and Information Science, Iwate Prefectural University, Iwate, Japan

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ABSTRACT

Summarization of online reviews by customers is a popular practice for evaluation of products or services. As the reviews accumulate, the large size and the unstructured nature of the reviews hinder manual summarization. Automatic categorization of the reviews as a whole into only positive and negative group cannot represent a clear picture. An aspect based automatic summarization technique can provide better visualization.  However, automatic extraction of proper aspects from the huge reviews of any product is not very easy. There are some research works in this direction, but any definite method is yet to come. In this work, a two-step Latent Dirichlet Allocation (LDA) technique, which is popularly used for topic modelling has been developed for efficient aspect extraction. The method has been evaluated by simulation experiments on Amazon product reviews and Yelp restaurant and hotel reviews. The results have been found quite matching with human annotated results.


Keywords: Opinion analysis, Aspect extraction, Review summarization, Two-step LDA.


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ARTICLE INFORMATION

Received: 2020-06-02
Revised: 2020-08-27
Accepted: 2020-12-16
Available Online: 2021-03-01


Cite this article:

Das, S.J., Murakami, R., Chakraborty, B. 2021. Development of a two-step LDA based aspect extraction technique for review summarization. International Journal of Applied Science and Engineering, 18, 2020120. https://doi.org/10.6703/IJASE.202103_18(1).003

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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