The system with the semantic analyzer has achieved accuracy over 90% in the sex identification in the real chat medium.ABSTRACT: Mining textual data in chat mediums is becoming more important because these mediums contain a vast amount of information, which is potentially relevant to a society’s current interests, habits, social behaviors, crime tendency and other tendencies.
Then, the proposed sex identification method is compared with the Support Vector Machine (SVM) and Naive Bayes (NB) methods.Finally, results show that the proposed system has achieved accuracy over 90% in sex identification. Although carefully collected, accuracy cannot be guaranteed.In today’s day and age I can totally understand if you want to know how to start a conversation online. Although carefully collected, accuracy cannot be guaranteed.The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Differing provisions from the publisher's actual policy or licence agreement may be applicable.
Chat mediums are becoming an important part of human life in societies and provide quite useful information about people such as their current interests, habits, social behaviors and tendencies.
In this study, we have presented an identification system to identify the sex of a person in a Turkish chat medium.
Here, the sex identification is taken as a base study in the information mining in chat mediums.
This system acquires data from a chat medium, and then automatically detects the chatter’s sex from the information exchanged between chatters and compares them with the known identities of the chatters.
To do this task, a simple discrimination function is used to determine the sex of the chatters.
A semantic analysis method is also proposed to enhance the performance of the system.