International Journal of Emerging Trends in Science & Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Sentiment Analysis on Verbatim Responses for Understanding Students' Living and Learning Experience


Affiliations
1 Associate Professor, Department of CSE, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, India
2 UG Student, Department of CSE, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, India
     

   Subscribe/Renew Journal


Sentimental Analysis the computational study of sentiments, people’s opinions and emotions expressed in written language. It is very popular because of the wide range of applications it possesses. Opinions are the key influences of our behaviours and decision-making. Opinion mining helps in taking the right decisions as the analyzed data reports give the right emotion on products, companies, services, public personalities, governments, etc. Students’ opinions on their institutions, teachers, and trainers are collected nowadays. But the collected opinions are not useful unless it is analyzed and the measures according to it are taken. The conventional method is followed by analyzing it manually. Yet it could not be the consistent method for time and resource saving. Hence, we go for the computational method for opinion mining using Machine Learning techniques for extracting the emotions of the students. Machine Learning algorithms have come a long way, with Naive Bayes, Support Vector Machine and Maximum Entropy is the feature used in research. Sentiment classification by different categories involving the sentiments is the topic of research. This paper presents the survey on Sentimental Analysis on students’ feedback to extract the emotions of the students with the text feedback.

Keywords

Sentimental Analysis (SA), Opinion Mining, Machine Learning (ML), Natural Language Processing (NLP), Student Feedback Analysis
User
Subscription Login to verify subscription
Notifications
Font Size

  • Jason Brownlee, “Develop Deep Learning Models for Natural Language in Python”, in Machine Learning Mastery, Edition: v1.1, 2017.

  • R. Socher, A. Perelygin, “Recursive deep models for semantic compositionality over a sentiment tree bank”, Empirical Methods of Natural Language Processing, October 2013.

  • Xin Chen, Mihaela and Krishna P.C, “Mining Social Media Data for Understanding Students Learning Experiences”, IEEE Transactions on Learning Technologies, 2014.

  • Manjunath B Talawar and Prasad.M. R, “Analyzing SocialMedia Data in Educational Sectors Using Data Mining Techniques”, International Journal of Computer Science and Information Technologies, Vol. 7 (4), 2016.

  • Eman M.G. Younis, “Sentiment Analysis and Text Mining for Social Media Microblogs using Open Source Tools: An Empirical Study”, International Journal of Computer Applications, 2015.

  • Aaquib Multani, “Sentiment Analysis for Understanding Students’ Learning Experiences: A survey Paper, International Journal of Scientific Research & Engineering Trends, Volume 5, Mar-Apr-2019.

  • Shanshan Yi and Xiafong Liu, “Machine Learning based customer sentiment analysis for recommending shoppers based on reviews”, Springer Nature in Complex & Intelligent Systems Complex & Intelligent Systems , Volume 6, pp 621-634, 2020.

  • Jianqiang Z, Xiaolin G, Xuejun Z, “Deep convolution neural networks for twitter sentiment analysis”, IEEE Access 6:23253–23260, 2018.

  • Tan S et al, “Interpreting the public sentiment variations on twitter”, IEEE transaction of knowledge Data Engineering, 2014.

  • Quratulain Rajput, Sajjad Haider, Zarmeen Nasim, “Sentiment analysis of student feedback using machine learning and lexicon based approaches”. In Proceedings of 5th ICRIIS, 2017.


Abstract Views: 177

PDF Views: 0




  • Sentiment Analysis on Verbatim Responses for Understanding Students' Living and Learning Experience

Abstract Views: 177  |  PDF Views: 0

Authors

P. Santhi
Associate Professor, Department of CSE, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, India
R. Kabilamoorthy
UG Student, Department of CSE, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, India

Abstract


Sentimental Analysis the computational study of sentiments, people’s opinions and emotions expressed in written language. It is very popular because of the wide range of applications it possesses. Opinions are the key influences of our behaviours and decision-making. Opinion mining helps in taking the right decisions as the analyzed data reports give the right emotion on products, companies, services, public personalities, governments, etc. Students’ opinions on their institutions, teachers, and trainers are collected nowadays. But the collected opinions are not useful unless it is analyzed and the measures according to it are taken. The conventional method is followed by analyzing it manually. Yet it could not be the consistent method for time and resource saving. Hence, we go for the computational method for opinion mining using Machine Learning techniques for extracting the emotions of the students. Machine Learning algorithms have come a long way, with Naive Bayes, Support Vector Machine and Maximum Entropy is the feature used in research. Sentiment classification by different categories involving the sentiments is the topic of research. This paper presents the survey on Sentimental Analysis on students’ feedback to extract the emotions of the students with the text feedback.

Keywords


Sentimental Analysis (SA), Opinion Mining, Machine Learning (ML), Natural Language Processing (NLP), Student Feedback Analysis

References