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A preliminary study on hybrid sentiment model for customer purchase intention analysis in socialcommerce

Eshak, M.I. and Ahmad, R. and Sarlan, A. (2018) A preliminary study on hybrid sentiment model for customer purchase intention analysis in socialcommerce. 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017, 2018-J . pp. 61-66.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

With the usage of Web 2.0 technologies, the internet users share their views via social media resulting in a large amount of raw data for which data mining techniques are needed to extract valuable knowledge. An example of people's views that can be extracted from the users' comments(tweets) is their interest or preference on different products, services, events etc. This research work aims to study on the customer intention to purchase in social-commerce(s-commerce). Finding out the intention of customer to purchase in s-commerce is important due to the major role being brought by the customers in marketing. Sentiment analysis is seen to be the most appropriate method to extract customer's opinions. This paper presents the fundamental part of the research for developing a hybrid sentiment analysis model by using machine learning approach and lexicon-based approach to analyze communications especially using Malay language on social network portals to determine the customer intention to purchase in s-commerce. Precision, recall, accuracy and f-measure metrics are proposed for the computing and comparing the results of the experiments. © 2017 IEEE.

Item Type:Article
Impact Factor:cited By 0; Conference of 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017 ; Conference Date: 16 November 2017 Through 17 November 2017; Conference Code:134594
Uncontrolled Keywords:Artificial intelligence; Behavioral research; Commerce; Data mining; Learning systems; Sales; Sentiment analysis, Customer intentions; Lexicon-based; Machine learning approaches; Malay languages; Purchase intention; Social commerces; Social network portals; Web 2.0 Technologies, Big data
ID Code:21774
Deposited By: Ahmad Suhairi
Deposited On:14 Aug 2018 00:45
Last Modified:14 Aug 2018 00:45

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