%0 Conference Paper
%A Eshak, M.I.
%A Ahmad, R.
%A Sarlan, A.
%D 2017
%F scholars:8532
%I Institute of Electrical and Electronics Engineers Inc.
%K Commerce; Data mining; Machine learning; Sales; Social networking (online), Customer intentions; Internet users; Lexicon-based; Machine-learning; Malay languages; Opinion mining; Purchase intention; Sentiment analysis; Social commerces; Web 2.0 Technologies, Sentiment analysis
%P 61-66
%R 10.1109/ICBDAA.2017.8284108
%T A preliminary study on hybrid sentiment model for customer purchase intention analysis in socialcommerce
%U https://khub.utp.edu.my/scholars/8532/
%V 2018-J
%X 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.
%Z cited By 10; Conference of 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017 ; Conference Date: 16 November 2017 Through 17 November 2017; Conference Code:134594