Faculty rating system based on student feedbacks using sentimental analysis

Krishnaveni, K.; Pai, Rohit R; Iyer, Vignesh

doi:10.1109/icacci.2017.8126079

Cited by 26 publications

(11 citation statements)

References 5 publications

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“…Table 8 shows papers that reported the sources of the datasets used for conducting experiments along with their corresponding categories and description. Here, the data were mostly collected by conducting surveys among students and teachers or by providing questioners to collect feedback from the students Education/research platforms [14,31,36,40,[44][45][46]48,58,61,70,78,82,84,86,93,95,99,101] This category contains the data extracted from online platforms providing different courses such as Coursera, edX, and research websites such as ResearchGate, LinkedIn, etc.…”

Section: Rq5 What Are the Most Common Sources Used To Collect Students' Feedback?mentioning

confidence: 99%

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

et al. 2021

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In the last decade, sentiment analysis has been widely applied in many domains, including business, social networks and education. Particularly in the education domain, where dealing with and processing students’ opinions is a complicated task due to the nature of the language used by students and the large volume of information, the application of sentiment analysis is growing yet remains challenging. Several literature reviews reveal the state of the application of sentiment analysis in this domain from different perspectives and contexts. However, the body of literature is lacking a review that systematically classifies the research and results of the application of natural language processing (NLP), deep learning (DL), and machine learning (ML) solutions for sentiment analysis in the education domain. In this article, we present the results of a systematic mapping study to structure the published information available. We used a stepwise PRISMA framework to guide the search process and searched for studies conducted between 2015 and 2020 in the electronic research databases of the scientific literature. We identified 92 relevant studies out of 612 that were initially found on the sentiment analysis of students’ feedback in learning platform environments. The mapping results showed that, despite the identified challenges, the field is rapidly growing, especially regarding the application of DL, which is the most recent trend. We identified various aspects that need to be considered in order to contribute to the maturity of research and development in the field. Among these aspects, we highlighted the need of having structured datasets, standardized solutions and increased focus on emotional expression and detection.

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Section: Rq5 What Are the Most Common Sources Used To Collect Students' Feedback?mentioning

confidence: 99%

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

et al. 2021

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“…A faculty rating system based on student feedback is proposed by [13]. For this purpose, the Naïve Bayes classification algorithm was implemented, and faculty was classified into different classes based on five-star rating.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy-Based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

Asghar¹,

Ullah²,

Shamshirband³

et al. 2019

Preprint

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The feedback collection and analysis has remained an important subject matter since long. The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis. However, the student expresses their feedback opinions on online social media sites, which need to be analyzed. This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews. Our technique computes the sentiment score of student feedback reviews and then applies fuzzy-logic module to analyze and quantify student’s satisfaction at the fine-grained level. The experimental results reveal that the proposed work has outperformed the baseline studies as well as state-of-the-art machine learning classifiers.

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“…Methods like Naïve Bayes, ID3, J48 Decision Tree are used. The system described in [6] evaluates faculty and rates them with certain specified parameter to improve academic and education standard. The system is based on attribute and uses multipoint rating system.…”

Section: Literature Surveymentioning

confidence: 99%

Analysis of Student Feedback using Deep Learning

S¹,

T²,

Ashwini³

2019

IJCATR

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Feedback plays a key role in improving quality. To ensure improvement in teaching method and facilities provided by college, opinion of the students should be properly analysed and used. Text Sentiment analysis method are used to carry out such analysis. It can be performed in two ways -Machine Learning approach and Lexicon based approach. Presently, the teacher evaluation and feedback analysis are based on identifying student's opinion. Methods used for such classification are Naive Bayes, Voting ensemble method. Along with determining polarity, classifying feedback as strength, weakness and suggestions can improve to be more beneficial. Success of deep learning inspires us to propose a better and efficient system. The System that will use Word2Vec for text processing, Convolution Neural Network for automatic feature extraction. Supervised Support Vector Machine will be used for final classification. The Proposed system will result in classification of feedback as Strength , Weakness and Suggestions to faculty.

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Faculty rating system based on student feedbacks using sentimental analysis

Cited by 26 publications

References 5 publications

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

Fuzzy-Based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

Analysis of Student Feedback using Deep Learning

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