Students Query Classification System
S Nithish Kumar1, M. Sai Subhakar2, S. Sumanth Reddy3, K Veeresh4, Venkataramana N5
1S Nithish Kumar*, Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, Vijayawada, India.
2M Sai Subhakar, Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, Vijayawada, India.
3S. Sumanth Reddy, Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, Vijayawada, India.
4K Veeresh , Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, Vijayawada, India.
5Venkataramana N, Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, Vijayawada, India.
Manuscript received on January 06, 2021. | Revised Manuscript received on January 23, 2021. | Manuscript published on January 30, 2021. | PP: 191-195 | Volume-9 Issue-5, January 2021. | Retrieval Number: 100.1/ijrte.E5247019521 | DOI: 10.35940/ijrte.E5247.019521
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A University or educational institute generally receives a bulk of complaints posted by students every day. The issues relate to their academics or any issues related to their education or related to exam sections etc., because of these bulk of complaints received from the students every day, makes it difficult for the university to sort out them and classify them and send them to their respective departments for resolving the issues. In this project, we work on classifying these complaints based on the classes or departments they belong to, using. By using TF-IDF (term frequency-inverse document frequency) it finds terms which are more related to a specific document by converting to vectors. By capturing some keywords in the complaints, adding some weight to the keywords and using different Machine Learning classification’s we are classifying the complaint based on these keywords. This classification makes the works easier for the university and saves time which is used to sort them and gives better service for the students. Now they can directly send the complaints to the respective departments with ease.
Keywords: Classification, Complaints, Departments, Machine Learning, TF-IDF (term frequency-inverse document frequency), Vectors.