Text Classification on Customer Review Dataset Using Support Vector Machine

被引：0
|
作者：
Bamgboye, Pelumi O. [1 ]
Adebiyi, Marion O. [1 ,4 ,5 ]
Adebiyi, Abayomi A. [1 ,4 ,5 ]
Osang, Francis B. [3 ]
Adebiyi, Ayodele A. [2 ]
Enwere, Miracle Nmesomachi [5 ,6 ]
Shekari, Abednego [5 ,6 ]
机构：
[1] Landmark Univ, Dept Comp Sci, Omu Aran, Nigeria
[2] Durban Univ Technol, Dept Elect Power Engn, Durban, South Africa
[3] Natl Open Univ Nigeria, Dept Comp Sci, Abuja, Nigeria
[4] Landmark Univ SDG 8 Decent Work & Econ Growth Nig, Abuja, Nigeria
[5] Covenant Appl Informat & Commun Africa Ctr Excell, Abuja, Nigeria
[6] Covenant Univ, Coll Sci & Technol, Dept Comp & Informat Sci, Ota, Ogun State, Nigeria
来源：
INTELLIGENT SUSTAINABLE SYSTEMS, WORLDS4 2022, VOL 2 | 2023年 / 579卷
关键词：
Text classification; Machine learning (ML); Customer review; Sentiment analysis;
D O I：
10.1007/978-981-19-7663-6_38
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Customer review is constantly generated in the form of text based on customer opinion concerning specific products in the electronic (e)-commerce space. With the proliferation of data, the classification of customers' opinions is a major issue in e-commerce. However, merchants frequently require customers to input their opinion about a product, in order to harvest customer experiences. The text classification technique helps customers and merchants to know if a product has a positive or negative review. This will enable merchants to improve the customer experience and also improve the revenue of the company. Sentiment analysis was executed on a customer product review dataset using support vector machine (SVM) in this study. The experimental results obtained show an accuracy of 86.67% in classifying customers' opinions on selected products. The findings will guide merchants to know customer feedback and identify their needs to improve sales culminating in increased revenue.
引用
收藏
页码：407 / 415
页数：9
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