Open Access

REVIEW

Analyzing Customer Reviews on Social Media via Applying Association Rule

Nancy Awadallah Awad^1,*, Amena Mahmoud²

1 Department of Computer and Information Systems, Sadat Academy for Management Sciences, Cairo, 11742, Egypt
2 Faculty of computers and Information, Department of Computer Science, Kafr el Sheikh University, Kafr El Sheikh, 33511, Egypt

* Corresponding Author: Nancy Awadallah Awad. Email:

Computers, Materials & Continua 2021, 68(2), 1519-1530. https://doi.org/10.32604/cmc.2021.016974

Received 15 January 2021; Accepted 17 February 2021; Issue published 13 April 2021

Abstract

The rapid growth of the use of social media opens up new challenges and opportunities to analyze various aspects and patterns in communication. In-text mining, several techniques are available such as information clustering, extraction, summarization, classification. In this study, a text mining framework was presented which consists of 4 phases retrieving, processing, indexing, and mine association rule phase. It is applied by using the association rule mining technique to check the associated term with the Huawei P30 Pro phone. Customer reviews are extracted from many websites and Facebook groups, such as re-view.cnet.com, CNET. Facebook and amazon.com technology, where customers from all over the world placed their notes on cell phones. In this analysis, a total of 192 reviews of Huawei P30 Pro were collected to evaluate them by text mining techniques. The findings demonstrate that Huawei P30 Pro, has strong points such as the best safety, high-quality camera, battery that lasts more than 24 hours, and the processor is very fast. This paper aims to prove that text mining decreases human efforts by recognizing significant documents. This will lead to improving the awareness of customers to choose their products and at the same time sales managers also get to know what their products were accepted by customers suspended.

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Keywords

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