service providers Identification in Telegram Persian messages based on feature selection methods

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Ebrahimi Farzaneh; Zare Chahooki Mohammad Ali; Hashemi Ali

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Journal Paper

Paper Information

Journal: Journal of Applied and Basic Machine Intelligence Research Year:2023 | Volume:1 | Issue:2 Page(s): 54-66

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Information Journal Paper

Title

service providers Identification in Telegram Persian messages based on feature selection methods

Author(s)

Ebrahimi Farzaneh | Zare Chahooki Mohammad Ali | Hashemi Ali | Issue Writer Certificate

Keywords

: classification

Service Provider Users

Feature selection

Feature Reduction

machine learning

Abstract

Telegram Messenger is a suitable platform for users who are looking to buy a product or receive services online. In these messengers, it is not possible to have direct access to the providers of goods and services, and in order to request the product, one must first become a member of the related groups and channels telegram. The purpose of this study is to directly identify users of service providers using the classification of Persian messages published in Telegram. One of the problems with categorizing these messages is the large size of the feature space, which reduces accuracy and increases classification time. Feature selection methods were used to solve this problem. The proposed method of this research is based on a combination of Feature selection methods based on local and global filters. In this regard, in the first step, using the most widely used methods for selecting local and global filter feature, related features are selected. In the second step, a combination of local and global filtering methods is used to identify better features and increase classification accuracy. The innovation of this research is in using the combined methods of Feature selection for automatic classification of Telegram Persian messages, in order to identify the users of the service provider. The proposed method, while reducing the number of features and selecting related features, improves the performance of classification and Identification of service providers.

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