Sentiment analysis is a process of judging people thinking, imaginations, thoughts, personality through their textual words, emotions, different types of pictures including emoji’s, paintings and behavior etc. Sentiment analysis is widely spread in multiple fields and the deployment of the approach for judging the sentiments of educational sector stakeholders is barely a researched field. Teacher and students are the backbone of our educational system who works behind the improvement of the educational infrastructure and development of any country. Their sentiments while teaching and learning both affect the future of our new generation.

In this review paper, we highlight different methods, approaches and tools related to sentiment analysis. Classification tools include Machine learning and lexicon analysis, whereas Statistical tools include ANOVA test, T-test etc.

Topics
Machine learning, Natural language processing, Students, Learning and learning models, Teaching, Review, Analysis of variance
1.
Vishal A.
Kharde
 and
S.S
Sonawane
 “
Sentiment Analysis of Twitter Data: Asurvey of Techniques
,”
International Journal of ComputerApplications(0975-8887)
, Volume
139
-No.
11
, April
2016
.
Google Scholar
 
2.
Sohail Imran
Khan
 and
Nabaz Nawazad
Abdullah
, ”
The Impact of staff training anddevelopment on teacher’s productivity
,” May 2019,
Article in Economics Management and sustainability
3.
AlessiaD’
Andrea
,
Fernando
Ferri
,
Patrizia
Grifoni
 and
Tiziana
Guzzo
, “
Approaches, Tools and Applications for sentiment analysis Implementation
,”
International Journal of Computer Applications(0975-8887)
, volume
125
(30, September
2015
.
Google Scholar
 
4.
Nabeela
Altrabsheh
,
Ella
Haig
 and
Sanaz
Fallahkhair
, ”
Learning Sentiment from sudents’ feedback for real-time Interventions in classroom
,” September
2014
.
Google Scholar
 
8.
A
Miller
,
R.
Beckwith
,
C.
Fellbaum
,
D.
Gross
, and
K.J.
Miller
, “
introduction to wordnet:an online lexical database
,”
International journal of Lexicography
, vol.
3
(
4
), pp.
235
–
244
,
1990
.
https://doi.org/10.1093/ijl/3.4.235
Google Scholar
Crossref
Search ADS
 
9.
S.
Mohammad
,
C.
Dunne
 and
B.
Dorr
, “Generating high coverage semantic orientation lexicons from overtly marked words andathesaurus,” in
preceedings of conference on Empirical methods in Natural Language Processing(EMNLP 09)
, vol
2
, pp.
599
–
608
Singapore, Augus
.
10.
Quratulain
Rajput
,
Sajjadaider
 and
Sayeed
Ghani
, “
Lexicon-based sentiment analysis of teachers’ evaluation
,”
Applied Computational Intelligence and So, Computing volume
2016
.
Google Scholar
 
11.
Archana
Rao P. N.
 and
Kishore
Baglodi
, ”
Role of sentiment analysis in education sector in the era of Big data: A survey
,”
International Journal of latest trend of engineering and Technology
, Special issue SACAIM
2017
, pp.
022
–
024
.
Google Scholar
 
12.
Jalal
Hanaysha
, “
Improving employee productivity through work engagement: Evidence from higher education sector
,” January
2016
,
International journal of Industrial Engineering Computations.
Google Scholar
 
14.
https://cyfar.org/types-statistical-tests (accessed on 04/03/2022)
15.
Vivek kumar
singh
,
Rajesh
Pryani
,
Auddin
 and
Pranav
Walia
, “
Sentiment analysis of movie review
,”
conference on IEEE
-
2013
.
Google Scholar
 
16.
ApporveAgarwal
,
BoyiXie
,
Ilia
Vovsha
,
Owen
rambow
, and
Rebecca
Passonneau
, “
Sentiment Analysis of Twitter data
”,
2009
.
Google Scholar
 
17.
Zhaoxia
Wang
,
Victor JooChuan
Tong
,
Xin
, and
Hoong Chor
Chin
, “
Anamaly detection through Enhanced seniment analysison social media
,”
^th International Conference on Cloudcomputing Technology andscience
,
2014
,
IEEE
.
Google Scholar
 
18.
Oladapo
Oyebode
,
RitaOrji
, ”Social media and Sentiment Analysis: The Nigeria Presidential Election 2019,
2019
,
IEEE
.
19.
Jasten Keneth D.
Treceñe
, “
Delving The Sentiments To Track Emotions In Gender Issues: A Plutchik-Based Sentiment Analysis In Students’ Learning Diaries
,”
International journal ofscientific & technologyresearch
, Vol
8
(
12
),
2019
.
Google Scholar
 
20.
Nabeela
Altrabsheh
,
Mihaela
Cocea
, and
Sanaz
Fallahkhair
, ”
Predicting Students’ Emotions Using Machine Learning Technique
”,
School of Computing, University of Portsmouth, conference paper
, June
2015
.
Google Scholar
 
21.
Kumar
Ravi
,
Ravi
Vadlamani
 and
Siddeshwar
Vasili
, ”
Sentiment analysis applied to educational sector
“, An article,
2015
.
Google Scholar
 
22.
Las Johansen B.
Caluza
, ”
Understanding the Influence of Mosston’s Spectrum of Teaching Styles Using Sentiment Analysis
,
Article in International Journal Social Sciences and Education
· October
2016
.
Google Scholar
 
23.
Mircea Radu
Georgescu
 and
Ioana Andreea
Bogoslov
, ”
Importance and opportunities of sentiment analysis in developing e-learning systems through social media
”, Accepted for publication
2019
.
Google Scholar
 
24.
Olivera
Grljević
,
Zita
Bošnjak
, ”
Sentiment discover and analysis as a mean of student experience improvement
,”,
Perspectives of Innovations, Economics & Business
, Volume
17
, Issue
1
,
2017
.
Google Scholar
 
25.
S.
Sridhar
,
M.
Vijayalakshmi
, “
Machine Learning
”,
Oxford University Press
,
2021
.
Google Scholar
PubMed
 
This content is only available via PDF.
© 2023 AIP Publishing LLC.
2023
AIP Publishing LLC
You do not currently have access to this content.