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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | SENTIMENTAL ANALYSIS FOR REAL TIME FEEDBACK USING ML ALGORITHMS |
| Authors Name: | N.VYSHNAVI , DR.A.PARIVAZHAGAN , P.RANGA VIKAS , K.CHARISHMA MADHAVI , S.GOVINDA RAO |
| Download E-Certificate: | Download |
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IJNRD_208448
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| Published Paper Id: | IJNRD2311131 |
| Published In: | Volume 8 Issue 11, November-2023 |
| DOI: | |
| Abstract: | In The research was conducted in order to present the student feedback system analysis model for improving the quality of teaching in academic institutions and universities. The system primarily utilizes a machine learning algorithm and textual feedback. This system has been configured to analyze student feedback in the form of comments, opinions, and reviews about teachers' performance. The textual feedback offers valuable insights into overall teaching quality and suggests valuable ways to improve teaching methodology. The purpose of this research is to look into various machine learning approaches and determine their importance. SVM, Random Forest, Nave Bayes algorithm, and lexical analysis are examples of machine learning techniques. SVM has the highest accuracy but requires more time to train for large datasets and is used for regression and classification to classify text. The collection contains data on the effectiveness of instruction and learning. This project looks at the textual comments in the text document to classify student feedback into positive, negative, and neutral categories. The system assists in reducing manual work by collecting feedback and storing it in a database accessible to authorized individuals. The teacher receives feedback analysis in the form of ratings and graphs, making data visualization easier. This system is an effective method for providing teachers with qualitative feedback that improves students' learning. |
| Keywords: | Feedback system, SVM algorithm, Machine learning, Naive Bayes. |
| Cite Article: | "SENTIMENTAL ANALYSIS FOR REAL TIME FEEDBACK USING ML ALGORITHMS", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 11, page no.b237-b241, November-2023, Available :http://www.ijnrd.org/papers/IJNRD2311131.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2311131 Registration ID: 208448 Published In: Volume 8 Issue 11, November-2023 DOI (Digital Object Identifier): Page No: b237-b241 Country: virudhnagar, tamilnadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2311131 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2311131 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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