Authors: Prof. Ravirai Chaudhary, Aniket M. Wazarkar, Shubham A. Patil, Nikhil S. Pokale, Prathamesh V. Mali
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We examine opinion mining using supervised learning techniques to discover the sentiment of student inputs supported by labeled teaching and learning decisions. The exams conducted included undergraduate input data collected from VR Siddhartha Engineering College, his Mixed Apparatus of AI, and the General Language Preparation System on a custom database collected using forms. In addition, in order to describe step-by-step techniques for obtaining opinions on or from scientific statements using open-source Python tools, this work demonstrates the overall performance of supporting arguments. We provide additional comparisons and extract alternatives. exams, apprenticeships, etc., are compared to find higher overall performance, and several scoring criteria have been developed for different techniques.
The existence of an enormous amount of ordered records pretty much like grades, conscription information, and development quotes likewise as unstructured in order like scholarly opinions articulated via surveys, it will become a time and useful source overwhelming to précis the facts by hand to gain information lead conclusions and alternatives. This paper focuses on using the Opinion Mining and sentiment analysis technique for classifying the student’s feedback obtained during the module evaluation survey that is conducted every semester to know the feedback of students with respect to various features of teaching and learning such as modules, teaching, assessments, etc.
In colleges to know the ongoing efficiency and to know the level of student satisfaction, in a year, three surveys are conducted viz. Student Satisfaction Survey, Module Evaluation Survey, and Blitz Survey through which students give their opinion about various factors related to teaching and learning at the institution. The module evaluation survey and student satisfaction survey are conducted electronically whereas the Blitz survey is implemented manually. In all these three cases, the data analysis is done manually which causes substantial delays in making appropriate decisions for improvement based on student concerns which result in less student satisfaction and less intake. To avoid this circumstance and to increase the revenue of the college, the proposed research is undertaken.
II. LITERATURE REVIEW
The data is collected from the students in the form of feedback from the college or the feedback form sent to students using third-party apps such as google forms, Zoho forms, Microsoft forms, etc. The feedback consists of textual reviews. Data Pre-processing is done as the next step for the same. After that, opinion classification is performed on the dataset and the results are obtained.
3. Entity and Feature Extraction: Feature Extraction aims to reduce the number of features in a dataset by creating new features from the existing ones In order to understand the polarity of opinion with respect to various features, the terms Module, Teacher, Exam, and Resources were chosen. These words and their synonyms have been applied to Rapid Miner's SelectAttributes and FilterRows operators for a functional understanding of polarity.
4. Model Training: Function extraction is the name for techniques that choose and /or integrate variables into functions. it successfully reduces the number of statistics to process whilst as it should be and completely describes the original dataset. The supervised learning algorithms that were used are SVM, NB, K-NN, and ANN. The Validation operator from Rapid Miner which allows simultaneously train and test the classifiers was employed. In particular, the facility of cross-validation was exploited for the input dataset by setting the value to 10. This meant that the data set was divided into 10 groups of which the first 9 groups were used as training and one group was used for testing. In the second run, a different combination of 9 sets became the training data and a new set became the testing data. The process was continued until all the permutations were finished.
5. Result Evaluation: To compare the performance of the four algorithms employed, Accuracy, Precision, and Recall values were calculated for each of the classifier algorithms by using the Performance Operator of Rapid Miner. Training, Testing, and Performance evaluation process will be undertaken.
The authors would like to acknowledge the support and guidance provided by management and guides of SKN Sinhgad Institute of Technology and Science, Lonavala for providing the necessary support and guidance in carrying out this work.
Opinion-based mining becomes accustomed removes the remarked normal alternatives and evaluation words from the contribution dataset. A Student Feedback Mining System is working to inquire about points and their slants as of understudy-produced criticism. This strategy will be useful to improve student knowledge and the educator\'s process for conveyance. Automating the student’s feedback may give several advantages together with saving price, time, and creating economical report generation, etc. the utilization of opining mining will facilitate in summarizing the feedback report effectively and evaluating school performance in the type of a summarized read might be helpful for the establishments. In the future assessment, we resolve in broad absolute conclusion mining of student feedback gathered from web-based forms. Similarly, key features of teaching and learning at College are extracted from the student\'s feedback and classified as positive and negative based on their polarity to analyze the feature that needs improvement. Improving the accuracy is also the futuristic scope of the project. We are also planning to improve the performance of the opinion mining process by using a map-reduce framework by which program-wise, and year-wise analysis can be run parallel.
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Copyright © 2022 Prof. Ravirai Chaudhary, Aniket M. Wazarkar, Shubham A. Patil, Nikhil S. Pokale, Prathamesh V. Mali. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.