Singleton spam review detection using classification techniques
In recent years, we have witnessed that online reviews are the most important resource of customers’ opinion. They are progressively more used by individuals and organizations to make purchase and business decisions. Unfortunately due to the reason of profit or fame, frauds produce deceptive reviews...
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2014
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my-utm-ep.417202020-06-28T07:28:43Z Singleton spam review detection using classification techniques 2014 Heydari, Atefeh TK Electrical engineering. Electronics Nuclear engineering In recent years, we have witnessed that online reviews are the most important resource of customers’ opinion. They are progressively more used by individuals and organizations to make purchase and business decisions. Unfortunately due to the reason of profit or fame, frauds produce deceptive reviews to hoodwink potential customers. Their activities mislead potential customers to make purchasing decision, organizations to reshape their business and opinion mining techniques to reach accurate results. Spam Review could be divided into two main groups i.e. multiple and singleton reviews. Detecting a singleton spam review that is the only review written by a user id is very difficult. Although singleton spam reviews are very harmful, various features and proofs used in multiple spam reviews detection are not applicable in this case. Current research aims to propose a technique to detect singleton spam reviews. To achieve this, various features were assessed and analyzed. Then the appropriate ones were selected to be combined with proposed novel methods in current research and used to be used in a classifier. The SVM and Naïve Bayes classification Algorithm were used for model building. The results revealed that SVM was more accurate than Naïve Bayes in classification finally combination of the most effective features was selected 2014 Thesis http://eprints.utm.my/id/eprint/41720/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:85904?queryType=vitalDismax&query=Singleton+spam+review+detection+using+classification+&public=true masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing |
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Universiti Teknologi Malaysia |
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UTM Institutional Repository |
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TK Electrical engineering Electronics Nuclear engineering |
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TK Electrical engineering Electronics Nuclear engineering Heydari, Atefeh Singleton spam review detection using classification techniques |
| description |
In recent years, we have witnessed that online reviews are the most important resource of customers’ opinion. They are progressively more used by individuals and organizations to make purchase and business decisions. Unfortunately due to the reason of profit or fame, frauds produce deceptive reviews to hoodwink potential customers. Their activities mislead potential customers to make purchasing decision, organizations to reshape their business and opinion mining techniques to reach accurate results. Spam Review could be divided into two main groups i.e. multiple and singleton reviews. Detecting a singleton spam review that is the only review written by a user id is very difficult. Although singleton spam reviews are very harmful, various features and proofs used in multiple spam reviews detection are not applicable in this case. Current research aims to propose a technique to detect singleton spam reviews. To achieve this, various features were assessed and analyzed. Then the appropriate ones were selected to be combined with proposed novel methods in current research and used to be used in a classifier. The SVM and Naïve Bayes classification Algorithm were used for model building. The results revealed that SVM was more accurate than Naïve Bayes in classification finally combination of the most effective features was selected |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Heydari, Atefeh |
| author_facet |
Heydari, Atefeh |
| author_sort |
Heydari, Atefeh |
| title |
Singleton spam review detection using classification techniques |
| title_short |
Singleton spam review detection using classification techniques |
| title_full |
Singleton spam review detection using classification techniques |
| title_fullStr |
Singleton spam review detection using classification techniques |
| title_full_unstemmed |
Singleton spam review detection using classification techniques |
| title_sort |
singleton spam review detection using classification techniques |
| granting_institution |
Universiti Teknologi Malaysia, Faculty of Computing |
| granting_department |
Faculty of Computing |
| publishDate |
2014 |
| _version_ |
1747816604343730176 |