Development of an Artificial Intelligence Based Correction System for Spelling Errors in Product Reviews

Okan Çiftçi; Sumru Nayir; Emre Tolga Ayan; Ceren Ulus; Mehmet Fatih Akay

Article Details

Development of an Artificial Intelligence Based Correction System for Spelling Errors in Product Reviews

Authors:

Okan Çiftçi | Sumru Nayir | Emre Tolga Ayan | Ceren Ulus | Mehmet Fatih Akay

Publisher:

Burdur Mehmet Akif Ersoy University

Main Topics:

Natural Language Processing

Journal:

Scientific Journal of Mehmet Akif Ersoy University Vol.7 Issue 2, pp.99-108

Document Type:

Article

Publication Date:

12/2024

Document Language:

English

Document ID (Org No.):

2001328418

pISSN:

2651-3722

Keywords:

Abstract:

 E-commerce has experienced rapid growth in recent years and continues to expand dynamically. In this sector, maximizing customer satisfaction and enhancing the shopping experience are recognized as important strategic initiatives. To maximize customer satisfaction, it is essential to accurately determine customer needs and provide appropriate solutions to meet demand. In this context, feedback obtained from customers holds significant importance. However, customer comments often contain spelling errors, complicating the analysis of these comments. This study aims to automatically correct spelling errors in user comments regarding products sold on the e-commerce site Trendyol.com. For this purpose, a system based on transformer architecture has been created. Various spelling error detection and correction models were subsequently developed based on this architecture. Prediction models have been developed using two separate datasets consisting of Trendyol user comments and two additional datasets, including the Turkish Spelling Check Dataset taken from the Hunspell library, and the effects of these four datasets on prediction performance have been examined. The success of the models has been evaluated using the Accuracy metric. The performance of the developed models was also compared with that of the model in the Zemberek library. As a result of the study, it has been observed that the utilization of the Turkish Spelling Check Dataset positively influences prediction performance. The developed system enhanced customer experience by correcting spelling errors in comments.

 E-commerce has experienced rapid growth in recent years and continues to expand dynamically. In this sector, maximizing customer satisfaction and enhancing the shopping experience are recognized as important strategic initiatives. To maximize customer satisfaction, it is essential to accurately determine customer needs and provide appropriate solutions to meet demand. In this context, feedback obtained from customers holds significant importance. However, customer comments often contain spelling errors, complicating the analysis of these comments. This study aims to automatically correct spelling errors in user comments regarding products sold on the e-commerce site Trendyol.com. For this purpose, a system based on transformer architecture has been created. Various spelling error detection and correction models were subsequently developed based on this architecture. Prediction models have been developed using two separate datasets consisting of Trendyol user comments and two additional datasets, including the Turkish Spelling Check Dataset taken from the Hunspell library, and the effects of these four datasets on prediction performance have been examined. The success of the models has been evaluated using the Accuracy metric. The performance of the developed models was also compared with that of the model in the Zemberek library. As a result of the study, it has been observed that the utilization of the Turkish Spelling Check Dataset positively influences prediction performance. The developed system enhanced customer experience by correcting spelling errors in comments.

 E-commerce has experienced rapid growth in recent years and continues to expand dynamically. In this sector, maximizing customer satisfaction and enhancing the shopping experience are recognized as important strategic initiatives. To maximize customer satisfaction, it is essential to accurately determine customer needs and provide appropriate solutions to meet demand. In this context, feedback obtained from customers holds significant importance. However, customer comments often contain spelling errors, complicating the analysis of these comments. This study aims to automatically correct spelling errors in user comments regarding products sold on the e-commerce site Trendyol.com. For this purpose, a system based on transformer architecture has been created. Various spelling error detection and correction models were subsequently developed based on this architecture. Prediction models have been developed using two separate datasets consisting of Trendyol user comments and two additional datasets, including the Turkish Spelling Check Dataset taken from the Hunspell library, and the effects of these four datasets on prediction performance have been examined. The success of the models has been evaluated using the Accuracy metric. The performance of the developed models was also compared with that of the model in the Zemberek library. As a result of the study, it has been observed that the utilization of the Turkish Spelling Check Dataset positively influences prediction performance. The developed system enhanced customer experience by correcting spelling errors in comments.

 E-commerce has experienced rapid growth in recent years and continues to expand dynamically. In this sector, maximizing customer satisfaction and enhancing the shopping experience are recognized as important strategic initiatives. To maximize customer satisfaction, it is essential to accurately determine customer needs and provide appropriate solutions to meet demand. In this context, feedback obtained from customers holds significant importance. However, customer comments often contain spelling errors, complicating the analysis of these comments. This study aims to automatically correct spelling errors in user comments regarding products sold on the e-commerce site Trendyol.com. For this purpose, a system based on transformer architecture has been created. Various spelling error detection and correction models were subsequently developed based on this architecture. Prediction models have been developed using two separate datasets consisting of Trendyol user comments and two additional datasets, including the Turkish Spelling Check Dataset taken from the Hunspell library, and the effects of these four datasets on prediction performance have been examined. The success of the models has been evaluated using the Accuracy metric. The performance of the developed models was also compared with that of the model in the Zemberek library. As a result of the study, it has been observed that the utilization of the Turkish Spelling Check Dataset positively influences prediction performance. The developed system enhanced customer experience by correcting spelling errors in comments.

Article Details

Development of an Artificial Intelligence Based Correction System for Spelling Errors in Product Reviews

Authors:

Okan Çiftçi | Sumru Nayir | Emre Tolga Ayan | Ceren Ulus | Mehmet Fatih Akay

Publisher:

Burdur Mehmet Akif Ersoy University

Main Topics:

Natural Language Processing

Journal:

Scientific Journal of Mehmet Akif Ersoy University Vol.7 Issue 2, pp.99-108

Document Type:

Article

Publication Date:

12/2024

Document Language:

English

Document ID (Org No.):

2001328418

pISSN:

2651-3722

Spelling Error System

Product Reviews

Transformer Architecture

Turkish Spelling Check Dataset

Zemberek

Issues

Article Details

Development of an Artificial Intelligence Based Correction System for Spelling Errors in Product Reviews

Authors: Okan Çiftçi | Sumru Nayir | Emre Tolga Ayan | Ceren Ulus | Mehmet Fatih Akay

Publisher: Burdur Mehmet Akif Ersoy University

Main Topics: Natural Language Processing

Journal: Scientific Journal of Mehmet Akif Ersoy University Vol.7 Issue 2, pp.99-108

Document Type: Article

Publication Date: 12/2024

Document Language: English

Document ID (Org No.): 2001328418

pISSN: 2651-3722

Spelling Error System

Product Reviews

Transformer Architecture

Turkish Spelling Check Dataset

Zemberek

Issues

Authors:

Okan Çiftçi | Sumru Nayir | Emre Tolga Ayan | Ceren Ulus | Mehmet Fatih Akay

Publisher:

Burdur Mehmet Akif Ersoy University

Main Topics:

Natural Language Processing

Journal:

Scientific Journal of Mehmet Akif Ersoy University Vol.7 Issue 2, pp.99-108

Document Type:

Article

Publication Date:

12/2024

Document Language:

English

Document ID (Org No.):

2001328418

pISSN:

2651-3722