Classification Technique of Interviewer-Bot Result using Naïve Bayes and Phrase Reinforcement Algorithms.

Sarosa, Moechammad; Junus, Mochammad; Hoesny, Mariana Ulfah; Sari, Zamah; Fatnuriyah, Martin

In recent years, both foreign and national companies tend to conduct English-based interviews when recruiting new employees. Consequently, college graduate must be ready for English-based interviews during the process of seeking employment. To meet these requirements potential candidates tend to practice conversing in English with someone who is proficient in the language. Nevertheless, it is not easy to have someone who is not only proficient in English, but also have a good understanding of common interview questions. This paper presents the development of a machine which is able to provide practice on English-based interviews, specifically on job interviews. Interviewer machine (interviewer bot) is expected to help students practice speaking English appropriately for job interview. The interviewer machine design uses words from a chat bot database named ALICE to mimic human intelligence that can be applied to a search engine using AIML. Naïve Bayes algorithm is used to classify the interview results into three categories: POTENTIAL, TALENT and INTEREST students. Furthermore, based on the classification result, the summary is made at the end of the interview session by using phrase reinforcement algorithms. By using this bot, students are expected to practice their listening and speaking skills, also to be familiar with the questions often asked in job interviews so that they can prepare the proper answers. In addition, the bot users could know their potential, talent and prospects in finding a job. Hence, they could apply to the appropriate companies. Based on the validation results of 50 respondents, the accuracy degree of interviewer chat-bot (interviewer engine) response obtained 86.93%.

NAIVE Bayes classification; REINFORCEMENT learning; ALGORITHMS; ENGLISH language ability testing; EMPLOYMENT interviewing

International Journal of Emerging Technologies in Learning, 2018, Vol 13, Issue 2, p33

1863-0383

Academic Journal

10.3991/ijet.v13i02.7173