Text classification of E-commerce product via Hidden Markov model
E-Commerce is one of business mediums to offer a variety of choices to consumers. The explosion of data and information lead to the use of machine learning models to predict and customize the product categorization from online stores. This paper presents a study to assess the performance of Hidden M...
| Published in: | Frontiers in Artificial Intelligence and Applications |
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| Main Author: | |
| Format: | Conference paper |
| Language: | English |
| Published: |
IOS Press BV
2019
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082082117&doi=10.3233%2fFAIA190058&partnerID=40&md5=b5242a59fb68d2f765a9b35a7b3dd546 |
| Summary: | E-Commerce is one of business mediums to offer a variety of choices to consumers. The explosion of data and information lead to the use of machine learning models to predict and customize the product categorization from online stores. This paper presents a study to assess the performance of Hidden Markov Model (HMM) in classifying e-commerce products. There are two parameter estimation approaches used in evaluating the HMM which are Baum-Welch and Viterbi Training algorithms. The results show that Baum-Welch algorithm performed better than Viterbi Training algorithm in estimating parameters of HMM. Hence, the former algorithm provides a better parameter estimation for the HMM in the study. © 2019 The authors and IOS Press. All rights reserved. |
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| ISSN: | 9226389 |
| DOI: | 10.3233/FAIA190058 |