Analysis of people’s opinions based on the vaccination procedure and e-commerce product reviews using XLnet framework

Abstract

Many people have been severely affected by the COVID-19 outbreak, which has left them anxious, terrified, and other difficult feelings. Since the introduction of coronavirus vaccinations, people’s emotional spectrum has broadened and become more sophisticated.We want to observe and interpret their sentiments using deep learning techniques in this work. The most efficient way to convey one’s thoughts and feelings right now is via social media, and using Twitter may help one better understand what is popular and what is going through other people’s minds. Analyzing and visualization of data play a vital role in Data Science; as customers over e-commerce increase, feedback/reviews shared by them increase significantly, and decisions by a new customer to buy a product or not rely on these reviews; reviews might falsely be displayed which may be involving in controlling if any products demand and supply so, reviews analyzing and visualizationto understand they are genuinely playing an important role over e-commerce nowadays. Our primary objective in conducting this study was to understand better the various perspectives individuals held on the vaccination process and reviews of products purchased online. As shown by the presented study, analysis and visualization approaches may be used to facilitate rapid and easy comprehension of e-commerce data, despite its high dimensionality.All correlation and non-correlation factors were mapped and examined, providing a comprehensive picture of the proposed data and its connection to other parameters.The proposed work provides an overview of sentiment observations across arguments and the relationships between parameters; it opens the door for modeling to extract some decision-making insights from the data, which can be used to improve the efficiency of application areas like product quality and customer satisfaction.

Keywords

E-commerceproduct COVID-19 vaccines NLTK CNN model XLnet model TextBlob

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References

TechWire Asia, Mapping E-commerce Companies in Southeast Asia. Retrieved from: https://techwireasia.com/2021/11/mapping-ecommerce-companies-in-southeast-asia/ [accessed: 01 May 2022], (2021).

Awajan

, Mohamad

and Al-Quran

, Sentiment Analysis Technique and Neutrosophic Set Theory for Mining and Ranking Big Data From Online Reviews, in IEEE Access 9 (2021), 47338–47353, doi: 10.1109/ACCESS.2021.3067844.

Peng

, You

, Lu

and Li

, Mining Review Unit Model for Online Review Analysis, in IEEE Access 8 (2020), 196826–196834. doi: 10.1109/ACCESS.2020.3033820.

Yang

, Li

, Wang

and Sherratt

R.S.

, Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning, in IEEE Access 8 (2020), 23522–23530, doi: 10.1109/ACCESS.2020.2969854.

Grossman

V.A.

, The COVID-19 vaccine: why the hesitancy? Journal of Radiology Nursing 40(2) (2021), 116–119.

Mathieu

, Ritchie

, Ortiz-Ospina

et al., A global database of COVID-19 vaccinations, Nature Human Behaviour 5(7) (2021), 947–953.

Trapani

and Curigliano

, COVID-19 vaccines in patients with cancer, The Lancet Oncology 22(6) (2021), 738–739.

Cabanillas

and Novak

, Allergy to COVID-19 vaccines: a current update, Allergology International 70(3) (2021), 313–318.

Huang

, Xie

, Rao

, Liu

, Poon

L.K.M.

and Wang

F.L.

, Lexicon-based sentiment convolutional neural networks for online review analysis, in IEEE Transactions on Affective Computing 13(3) (2022), 1337–1348, doi: 10.1109/TAFFC.2020.2997769.

10.

Ali

, Wang

and Riaz

, Aspect based sentiment analysis of ridesharing platform reviews for kansei engineering, in 86–96, IEEE Access 8 (1731)–10.1109/ACCESS.2020.3025823.

11.

Kastrati

, Imran

A.S.

and Kurti

, Weakly Supervised Framework for Aspect-Based Sentiment Analysis on Students’ Reviews of MOOCs, in IEEE Access 8,(2020), 106799–106810, doi: 10.1109/ACCESS.2020.3000739.

12.

Zhang

and Zhong

, Mining users trust from e-commerce reviews based on sentiment similarity analysis, in IEEE Access 7 (2019), 13523–13535, doi: 10.1109/ACCESS.2019.2893601.

13.

Zhao

, Wang

, Sun

, Liu

, Fan

and Xuan

, What factors influence online product sales? online reviews, review system curation, online promotional marketing and seller guarantees analysis, in IEEE Access 8 (2020), 3920–3931, doi: 10.1109/ACCESS.2019.2963047.

14.

Viggiato

, Lin

, Hindle

and Bezemer

C.-P.

, What causes wrong sentiment classifications of game reviews? in IEEE Transactions on Games 14(3) (2022), 350–363, doi: 10.1109/TG.2021.3072545.

15.

Yang

, Xiong

, Cao

, Li

and Huang

, A Decision Method for Online Purchases Considering Dynamic Information Preference Based on Sentiment Orientation Classification and Discrete DIFWA Operators, in IEEE Access 7 (2019), in 77008–77026. doi: 10.1109/ACCESS.2019.2921403.

16.

Huang

, Dou

, Hu

and Huang

, Textual Analysis for Online Reviews: A Polymerisation Topic Sentiment Model, in IEEE Access 7 (2019), in 91940–91945, doi: 10.1109/ACCESS.2019.2920091.

17.

Kausar

, Huahu

, Ahmad

, Shabir

M.Y.

and Ahmad

, A sentiment polarity categorisation technique for online product reviews, in IEEE Access 8 (2020), 3594–3605, doi: 10.1109/ACCESS.2019.2963020.

18.

Zhao

, Gao

, Wen

and Li

, Combination of Convolutional Neural Network and Gated Recurrent Unit for Aspect-Based Sentiment Analysis, in IEEE Access 9 (2021), 15561–15569, doi: 10.1109/ACCESS.2021.3052937.

19.

Sweidan

A.H.

, El-Bendary

and Al-Feel

, Sentence-Level Aspect-Based Sentiment Analysis for Classifying Adverse Drug Reactions (ADRs) Using Hybrid Ontology-XLNet Transfer Learning, in IEEE Access 9 (2021), 90828–90846, doi: 10.1109/ACCESS.2021.3091394.

20.

Polisena

, Andellini

, Salerno

, Borsci

, Pecchia

and Iadanza

, Case Studies on the Use of Sentiment Analysis to Assess the Effectiveness and Safety of Health Technologies: A Scoping Review, in IEEE Access 9 (2021), 66043–166051, doi: 10.1109/ACCESS.2021.3076356.

21.

Maitama

J.Z.

, Idris

, Abdi

, Shuib

and Fauzi

, A systematic review on implicit and explicit aspect extraction in sentiment analysis, in IEEE Access 8 (2020), 194166–194191, doi: 10.1109/ACCESS.2020.3031217.

22.

Abo

M.E.M.

, Raj

R.G.

and Qazi

, A Review on Arabic Sentiment Analysis: State-of-the-Art, Taxonomy and Open Research Challenges, in IEEE Access 7 (2019), 162008–162024, doi: 10.1109/ACCESS.2019.2951530.

23.

Abdullah

N.A.

, Feizollah

, Sulaiman

and Anuar

N.B.

, Challenges and recommended solutions in multi-source and multi-domain sentiment analysis, in IEEE Access 7 (2019), 144957–144971, doi: 10.1109/ACCESS.2019.2945340.

24.

Smetanin

, The applications of sentiment analysis for russian language texts: current challenges and future perspectives, in IEEE Access 8 (2020), 110693–110719, doi: 10.1109/ACCESS.2020.3002215.

25.

Sharma

Sanjeev Kumar

, Sentiment Analysis: An analysis on its past, present and future scope, International Journal of Advanced Research in Science and Engineering 6(07) (2017).

26.

Agnihotri

Deepak

, Verma

Kesari

and Tripathi

Priyanka

, Computing Correlative Association of Terms for Automatic Classification of Text Documents. In ∼ Proceedings of the Third International Symposium on Computer Vision and the Internet. ACM, (2016), 71–80.

27.

Uysal

, An Improved Global Feature Selection Scheme for Text Classification, Expert Systems with Applications, Elsevier 43 (2016), 82–92.

28.

Nigam

, McCallum

A.K.

, Thrun

and Mitchell

, Text classification from labelled and unlabelled documents using EM, Machine Learning 39 (2000), 103–134.

29.

Urbanowicz

R.J.

, Meeker

, Cava

, Olson

R.S.

and Moore

J.H.

, Relief-based feature selection: Introduction and review, J. Biomed. Inform. 85 (2018), 189–203.

30.

and Guo

, Learning SVM Classifiers with Indefinite Kernels; Proceedings of the 26th Conference on Artificial Intelligence (AAAI); Toronto, ON, Canada. 22–26 July, 2012.

31.

Abualigah

, Elaziz

M. Abd

, Sumari

, Geem

Z.W.

and Gandomi

A.H.

, Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer, Expert Syst. Appl. 191 (2022), 116158.

32.

Gou

et al., A sgeneralised mean distance-based k-nearest neighbor classifier, Expert Syst. Appl 115 (2019), 356–372.

33.

Lan

Zhenzhong

, Chen

Mingda

, Goodman

Sebastian

, Gimpel

Kevin

, Sharma

Piyush

and Soricut

Radu

, Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942, 2019.

34.

Dataset Github Link.

35.

Jung

and Hu

, Ak-fold averaging cross-validation procedure, J. Non-param. Statist. 27(2) (2015), 167–179.

36.

Dora

, Agrawal

, Panda

and Abraham

, Nested cross-validation based adaptive sparse representation algorithm and its application to pathological brain classification, Expert Syst. Appl 114 (2018), 313–321.

37.

Dey

, Chakraborty

, Biswas

, Bose

and Tiwari

, Sentiment analysis of review datasets using Naive Bayes and K-NN classifier, Oct. 2016, arXiv:1610.09982. [Online]. Available: https://arxiv.org/abs/1610.09982.

38.

Huq

M.R.

, Ali

and Rahman

, Sentiment analysis on Twitter data using KNN and SVM, Int. J. Adv. Comput. Sci. Appl. 8(6) (2017), 19–25.

39.

Lakshmi

B.S.

, Raj

P.S.

and Vikram

R.R.

, Sentiment analysis using deep learning technique CNN with KMeans, Int. J. Pure Appl. Math. 114(11/2) (2017), 47–57.

40.

Shin

, Lee

and Choi

J.D.

, Lexicon integrated CNN models with attention for sentiment analysis, Oct. 2016, arXiv:1610.06272. [Online]. Available: https://arxiv.org/abs/1610.06272.

41.

Chen

, Zhuo

and Ren

, Gated recurrent neural network with sentimental relations for sentiment classification, Inf. Sci. 502 (2019), 268–278.

42.

Zhou

and Bian

, Improved text sentiment classification method based on BiGRU-attention, J. Phys., Conf. Ser. 1345 (2019), Art. no 032097.

43.

Yang

, Li

, Wang

and Sherratt

R.S.

, Sentiment analysis for e-commerce product reviews in chinese based on sentiment lexicon and deep learning, in IEEE Access 8 (2020), 23522–23530. doi: 10.1109/ACCESS.2020.2969854.