Vader lexicon and support vector machine algorithm to detect customer sentiment orientation

Vivine Nurcahyawati, . and Zuriani, Mustaffa (2023) Vader lexicon and support vector machine algorithm to detect customer sentiment orientation. Journal of Information Systems Engineering and Business Intelligence (JISEBI), 9 (1). pp. 108-118. ISSN 2443-2555. (Published)

Vader Lexicon and Support Vector Machine Algorithm.pdf
Available under License Creative Commons Attribution.

Download (838kB) | Preview


Background: The concept of customer orientation, which is based on a set of fundamental beliefs that prioritize the interests of the customer, requires companies to detect these interests in order to maintain a high level of quality in their products or services. Furthermore, there are several indicators of customer orientation, and one of them is their opinion or taste, which provides valuable feedback for businesses. With the rapid development of social media, customers can express emotions, thoughts, and opinions about services or products that may not be easily conveyed in the real world. Objective: The objective of this study is to detect customer orientation towards product or service quality, as expressed in online or social media. Additionally, the study showcases the novelty and superiority of the annotation process used for detecting customer orientation classifications. Methods: This study employs a method to compare the classification performance of the Vader lexicon annotation process with manual annotation. To accomplish this, a dataset from the Amazon website will be analyzed and classified using the Support Vector Machine algorithm. The objective of this method is to determine the level of customer orientation present within the dataset. To evaluate the effectiveness of the Vader lexicon, the study will compare the results of manual and automatic data annotation. Results: The results showed that customer orientation towards product or service quality has a predominantly positive value, comprising up to 76% of the total responses analyzed. Conclusion: The findings demonstrate that using Vader in the annotation process results in superior accuracy values compared to manual annotation. Specifically, the accuracy value increased from 86% to 88.57%, indicating that Vader could be a reliable tool for annotating text. Therefore, future studies should consider using Vader as a classifier or integrating it into the annotation process to further enhance its performance.

Item Type: Article
Uncontrolled Keywords: Classification, Customer, Orientation, Text analysis, Vader lexicon
Subjects: H Social Sciences > HF Commerce
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division: Institute of Postgraduate Studies
Faculty of Computing
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 07 Jul 2023 07:09
Last Modified: 07 Jul 2023 07:09
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item