Vader lexicon and support vector machine algorithm to detect customer sentiment orientation
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 indic...
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| Format: | Article |
| Language: | English |
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Universitas Airlangga
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/37968/1/Vader%20Lexicon%20and%20Support%20Vector%20Machine%20Algorithm.pdf |
| _version_ | 1825815029465743360 |
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| author | Vivine Nurcahyawati, . Zuriani, Mustaffa |
| author_facet | Vivine Nurcahyawati, . Zuriani, Mustaffa |
| author_sort | Vivine Nurcahyawati, . |
| collection | UMP |
| description | 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. |
| first_indexed | 2024-03-06T13:07:21Z |
| format | Article |
| id | UMPir37968 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:07:21Z |
| publishDate | 2023 |
| publisher | Universitas Airlangga |
| record_format | dspace |
| spelling | UMPir379682023-07-07T07:09:10Z http://umpir.ump.edu.my/id/eprint/37968/ Vader lexicon and support vector machine algorithm to detect customer sentiment orientation Vivine Nurcahyawati, . Zuriani, Mustaffa HF Commerce QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) 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. Universitas Airlangga 2023-04 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/37968/1/Vader%20Lexicon%20and%20Support%20Vector%20Machine%20Algorithm.pdf 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) https://doi.org/10.20473/jisebi.9.1.108-118 https://doi.org/10.20473/jisebi.9.1.108-118 |
| spellingShingle | HF Commerce QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) Vivine Nurcahyawati, . Zuriani, Mustaffa Vader lexicon and support vector machine algorithm to detect customer sentiment orientation |
| title | Vader lexicon and support vector machine algorithm to detect customer sentiment orientation |
| title_full | Vader lexicon and support vector machine algorithm to detect customer sentiment orientation |
| title_fullStr | Vader lexicon and support vector machine algorithm to detect customer sentiment orientation |
| title_full_unstemmed | Vader lexicon and support vector machine algorithm to detect customer sentiment orientation |
| title_short | Vader lexicon and support vector machine algorithm to detect customer sentiment orientation |
| title_sort | vader lexicon and support vector machine algorithm to detect customer sentiment orientation |
| topic | HF Commerce QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/37968/1/Vader%20Lexicon%20and%20Support%20Vector%20Machine%20Algorithm.pdf |
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