A CNN-BiLSTM deep model for intern detection

With the increasing desire of companies and organizations to employ interns in various situations, choosing the right person to participate in internships has become very important. Although the person who is selected for an internship must have relative knowledge and skills in the desired work fields; it should not be expert and experienced; because such people usually demand high wages. Community inquiry websites with many users can be used as one of the sources of intern knowledge. In previous research, statistical characteristics such as the number of answers, the number of specialized areas, the length of answers, and similar features have been proposed to identify potential interns; but the content of the user's answers has not been used to recognize the interns. This textual content is a rich resource for determining the breadth or depth of user knowledge and can be of great help in identifying potential trainees. In this research, a deep learning model called CNN-BiLSTM has been proposed to identify suitable people for internships based on the text of the answers they send to community inquiry websites. In addition, three machine learning models and four widely used deep learning models have also been used for comparison. Based on the obtained results, deep learning models have performed better in comparison with machine learning algorithms based on accuracy and F1 criteria. Also, among deep learning models, the proposed model has been able to show at least 7% higher accuracy and 2% higher F1 criterion than other models used to identify potential trainees.