Abstract
Sentiment analysis is a natural language processing method used to assess data's positivity, negativity, and neutrality. Several techniques were suggested as ways to solve the sentiment analysis task. This study presents a novel multi-criteria decision-making (MCDM) and game theory-based mathematical framework for the sentiment orientation of reviews. We propose two frameworks: sentiment orientation tagger modal (SOTM) and aspect-based ranking modal (ABRM). The SOTM consists of the simple additive weighting (SAW) technique and the principle of Nash equilibrium from game theory to deduce the tag for the review dataset. We identify a review's sentiment as positive, negative, or neutral. In ABRM, we rank the aspects of the review using the preference selection index (PSI). We propose an unsupervised sentiment classification model that combines context, rating, and emotion scores with a mathematical optimization model. The effectiveness of our proposed model is comparable to the state-of-the-art models, as demonstrated by experimental results on three benchmark review datasets. We also establish the significance of the results through statistical analysis. The proposed model ensures rationality and consistency. The novel combination of the MCDM and game theory model with the reviews' context, rating, and emotion scores creates a new paradigm in sentiment analysis. Also, the proposed model is generalizable and can analyze sentiment in many fields.
Similar content being viewed by others
Data availability
Zomato reviews: https://www.zomato.com/ncr/33-food-malviya-nagar-new-delhi/reviews Swiggy reviews: https://www.kaggle.com/code/residentmario/exploring-tripadvisor-uk-restaurant-reviews/notebook. Yelp reviews https://www.kaggle.com/datasets/omkarsabnis/yelp-reviews-dataset. TripAdvisor reviews https://www.kaggle.com/code/residentmario/exploring-tripadvisor-uk-restaurant-reviews/notebook.
Code availability
The code generated during the current study is available from the corresponding author on reasonable request.
Notes
From nltk tokenize import word_tokenize.
From nltk.stem import WordNetLemmatizer.
From nltk.corpus import stopwords.
References
Athanasiou V, Maragoudakis M (2017) A novel, gradient boosting framework for sentiment analysis in languages where NLP resources are not plentiful: a case study for modern greek. Algorithms 10:34. https://doi.org/10.3390/a10010034
Berka P (2020) Sentiment analysis using rule-based and case-based reasoning. J Intell Inform Syst 55:51–66. https://doi.org/10.1007/S10844-019-00591-8/TABLES/1
Zhou T, Law KMY (2022) Semantic relatedness enhanced graph network for aspect category sentiment analysis. Expert Syst Appl 195:116560. https://doi.org/10.1016/J.ESWA.2022.116560
Zhang S, Ly L, Mach N, Amaya C (2022) Topic modeling and sentiment analysis of yelp restaurant reviews. Int J Inform Syst Serv Sect 14:1–16. https://doi.org/10.4018/ijisss.295872
Fikri M, Sarno R (2019) A comparative study of sentiment analysis using SVM and SentiWordNet. Indones J Electr Eng Comput Sci 13:902–909. https://doi.org/10.11591/IJEECS.V13.I3.PP902-909
Sangkaew N, Zhu H (2022) Understanding tourists’ experiences at local markets in phuket: an analysis of tripadvisor reviews. J Qual Assur Hosp Tour 23:89–114. https://doi.org/10.1080/1528008X.2020.1848747
Huang F, Yuan C, Bi Y et al (2022) Multi-granular document-level sentiment topic analysis for online reviews. Appl Intell 52:7723–7733. https://doi.org/10.1007/S10489-021-02817-1/TABLES/6
Mohammad SM, Zhu X, Kiritchenko S, Martin J (2015) Sentiment, emotion, purpose, and style in electoral tweets. Inf Process Manag 51:480–499. https://doi.org/10.1016/J.IPM.2014.09.003
Giatsoglou M, Vozalis MG, Diamantaras K et al (2017) Sentiment analysis leveraging emotions and word embeddings. Expert Syst Appl 69:214–224. https://doi.org/10.1016/J.ESWA.2016.10.043
Bravo-Marquez F, Mendoza M, Poblete B (2014) Meta-level sentiment models for big social data analysis. Knowl-Based Syst 69:86–99. https://doi.org/10.1016/J.KNOSYS.2014.05.016
Bollegala D, Weir D, Carroll J (2013) Cross-domain sentiment classification using a sentiment sensitive thesaurus. IEEE Trans Knowl Data Eng 25:1719–1731. https://doi.org/10.1109/TKDE.2012.103
Liu M, Zhou F, Chen K, Zhao Y (2021) Co-attention networks based on aspect and context for aspect-level sentiment analysis. Knowl-Based Syst 217:106810. https://doi.org/10.1016/J.KNOSYS.2021.106810
Chen F, Xia J, Gao H et al (2021) TRG-DAtt: the target relational graph and double attention network based sentiment analysis and prediction for supporting decision making. ACM Trans Manag Inform Syst (TMIS) 13:1–25. https://doi.org/10.1145/3462442
Žunić A, Corcoran P, Spasić I (2021) Aspect-based sentiment analysis with graph convolution over syntactic dependencies. Artif Intell Med 119:102138. https://doi.org/10.1016/J.ARTMED.2021.102138
Lu Q, Zhu Z, Zhang G et al (2021) Aspect-gated graph convolutional networks for aspect-based sentiment analysis. Appl Intell 51:4408–4419. https://doi.org/10.1007/S10489-020-02095-3/FIGURES/5
Donadi M (2018) A system for sentiment analysis of online-media with tensorflow. 1–44
Lin C, He Y, Everson R, Rüger S (2012) Weakly supervised joint sentiment-topic detection from text. IEEE Trans Knowl Data Eng 24:1134–1145. https://doi.org/10.1109/TKDE.2011.48
Kim S, Zhang J, Chen Z, et al (2013) A hierarchical aspect-sentiment model for online reviews. In: Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013 526–533. https://doi.org/10.1609/aaai.v27i1.8700
Xu X, Cheng X, Tan S et al (2013) Aspect-level opinion mining of online customer reviews. China Commun 10:25–41. https://doi.org/10.1109/CC.2013.6488828
García-Pablos A, Cuadros M, Rigau G (2017) W2VLDA: almost unsupervised system for aspect based sentiment analysis. Expert Syst Appl 91:127–137. https://doi.org/10.1016/j.eswa.2017.08.049
Bu Z, Li H, Cao J et al (2016) Game theory based emotional evolution analysis for Chinese online reviews. Knowl-Based Syst 103:60–72. https://doi.org/10.1016/j.knosys.2016.03.026
Tripodi R, Linguistics MP-C (2017) Undefined A game-theoretic approach to word sense disambiguation. direct.mit.edu
Jain G, Lobiyal DK (2022) Word sense disambiguation using cooperative game theory and fuzzy hindi wordnet based on ConceptNet. Trans Asian Low-Resour Languag Inform Proce 21:1–25. https://doi.org/10.1145/3502739
Ahmad A, Ahmad T (2019) A Game Theory Approach for Multi-document Summarization. Arab J Sci Eng 44:3655–3667. https://doi.org/10.1007/S13369-018-3619-Y
Hossain N, Bhuiyan MR, Tumpa ZN, Hossain SA (2020) Sentiment analysis of restaurant reviews using combined CNN-LSTM. In: 2020 11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020. https://doi.org/10.1109/ICCCNT49239.2020.9225328
Basiri ME, Nemati S, Abdar M et al (2021) ABCDM: an attention-based bidirectional CNN-RNN deep model for sentiment analysis. Futur Gener Comput Syst 115:279–294. https://doi.org/10.1016/J.FUTURE.2020.08.005
Tripathy A, Anand A, Rath SK (2017) Document-level sentiment classification using hybrid machine learning approach. Knowl Inf Syst 53:805–831. https://doi.org/10.1007/S10115-017-1055-Z/FIGURES/5
Feng S, Wang D, Yu G et al (2010) Extracting common emotions from blogs based on fine-grained sentiment clustering. Knowl Inform Syst 27:281–302. https://doi.org/10.1007/S10115-010-0325-9
Saxena A, Mangal M, Jain G (2021) KeyGames: a game theoretic approach to automatic keyphrase extraction. 2037–2048. https://doi.org/10.18653/v1/2020.coling-main.184
Jain M, Suvarna A, Jain A (2021) An evolutionary game theory based approach for query expansion. Multimed Tools Appl. https://doi.org/10.1007/S11042-021-11297-X
Barfar A (2022) A linguistic/game-theoretic approach to detection/explanation of propaganda. Expert Syst with Appl 189:116069. https://doi.org/10.1016/J.ESWA.2021.116069
Punetha N, Jain G (2023) Bayesian game model based unsupervised sentiment analysis of product reviews. Expert Syst Appl 214:119128. https://doi.org/10.1016/J.ESWA.2022.119128
Mardani A, Jusoh A, Zavadskas EK et al (2016) Proposing a new hierarchical framework for the evaluation of quality management practices: a new combined fuzzy hybrid MCDM approach. Taylor Francis 17:1–16. https://doi.org/10.3846/16111699.2015.1061589
Afshari A, Mojahed M, Yusuff R (2010) Simple additive weighting approach to personnel selection problem. Int J Innov Manage Technol 1:511–515
Esuli A, Sebastiani F (2006) SENTIWORDNET: A publicly available lexical resource for opinion mining. In: Proceedings of the 5th International Conference on Language Resources and Evaluation, LREC 2006 417–422
Jagdale RS, Deshmukh SS (2020) Sentiment Classification on Twitter and Zomato Dataset Using Supervised Learning Algorithms. In: Proceedings of the 2020 International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing, ICSIDEMPC 2020 330–334. https://doi.org/10.1109/ICSIDEMPC49020.2020.9299582
Anas SM, Kumari S (2021) Opinion mining based fake product review monitoring and removal system. In:Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021 985–988. https://doi.org/10.1109/ICICT50816.2021.9358716
Ren X, Sun S, Yuan R (2021) A study on selection strategies for battery electric vehicles based on sentiments, analysis, and the MCDM model. Math Probl Eng. https://doi.org/10.1155/2021/9984343
Al Omari M, Al-Hajj M, Hammami N, Sabra A (2019) Sentiment classifier: logistic regression for arabic services’ reviews in lebanon. In: 2019 International Conference on Computer and Information Sciences, ICCIS 2019. https://doi.org/10.1109/ICCISci.2019.8716394
Win MN, Ravana SDR, Shuib L (2022) Sentiment attribution analysis with hierarchical classification and automatic aspect categorization on online user reviews. Malays J Comput Sci 35:89–110. https://doi.org/10.22452/MJCS.VOL35NO2.1
Khotimah DAK, Sarno R (2018) Sentiment detection of comment titles in booking.com using probabilistic latent semantic analysis
Billyan B, Sarno R, Sungkono KR, Tangkawarow IRHT (2019) Fuzzy k-nearest neighbor for restaurants business sentiment analysis on tripadvisor. In: 2019 International Conference on Information and Communications Technology, ICOIACT 2019 543–548. https://doi.org/10.1109/ICOIACT46704.2019.8938564
Laksono RA, Sungkono KR, Sarno R, Wahyuni CS (2019) Sentiment analysis of restaurant customer reviews on tripadvisor using naïve bayes. In: Proceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019 49–54. https://doi.org/10.1109/ICTS.2019.8850982
Yu SM, Wang J, Wang JQ (2017) An interval type-2 fuzzy likelihood-based MABAC approach and its application in selecting hotels on a tourism website. Int J Fuzzy Syst 19:47–61. https://doi.org/10.1007/S40815-016-0217-6/TABLES/7
Vyas V, Uma V, Ravi K (2020) Aspect-based approach to measure performance of financial services using voice of customer. J King Saud Univ Comput Inform Sci. https://doi.org/10.1016/j.jksuci.2019.12.009
Biaou BOS, Oluwatope AO, Odukoya HO et al (2020) Ayo game approach to mitigate free riding in peer-to-peer networks. J King Saud Univ Comput Inform Sci. https://doi.org/10.1016/j.jksuci.2020.09.015
Vincent TL, Brown JS (2005) Evolutionary game theory, natural selection, and darwinian dynamics
Seydel J (2006) Data envelopment analysis for decision support. Ind Manag Data Syst 106:81–95. https://doi.org/10.1108/02635570610641004
Madani K, Lund JR (2012) California’s sacramento-san joaquin delta conflict: from cooperation to chicken. J Water Resour Plan Manag 138:90–99. https://doi.org/10.1061/(asce)wr.1943-5452.0000164
Maniya K, Bhatt MG (2010) A selection of material using a novel type decision-making method: preference selection index method. Mater Des 31:1785–1789. https://doi.org/10.1016/J.MATDES.2009.11.020
Singh T, Patnaik A, Gangil B, Chauhan R (2015) Optimization of tribo-performance of brake friction materials: effect of nano filler. Wear 324–325:10–16. https://doi.org/10.1016/J.WEAR.2014.11.020
Rasiulis R, Ustinovichius L, Vilutiene T, Popov V (2016) Decision model for selection of modernization measures: public building case. J Civ Eng Manag 22:124–133. https://doi.org/10.3846/13923730.2015.1117018
Gojali S, Khodra ML (2016) Aspect based sentiment analysis for review rating prediction; Aspect based sentiment analysis for review rating prediction
Afzaal M, Usman M, Fong ACM et al (2016) Fuzzy aspect based opinion classification system for mining tourist reviews. Adv Fuzzy Syst 2016. https://doi.org/10.1155/2016/6965725
Zuheros C, Martínez-Cámara E, Herrera-Viedma E, Herrera F (2021) Sentiment analysis based multi-person multi-criteria decision making methodology using natural language processing and deep learning for smarter decision aid. case study of restaurant choice using tripadvisor reviews. Inform Fusion 68:22–36. https://doi.org/10.1016/J.INFFUS.2020.10.019
Hemalatha S, Ramathmika R (2019) Sentiment analysis of yelp reviews by machine learning. In: 2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019 700–704. https://doi.org/10.1109/ICCS45141.2019.9065812
Govindarajan M (2014) Sentiment Analysis Of Restaurant Reviews Using Hybrid Classification Method. Chennai India ISBN: 978–93
Nasim Z, Haider S (2017) ABSA toolkit: an open source tool for aspect based sentiment analysis. International Journal on Artificial Intelligence Tools. https://doi.org/10.1142/S0218213017500233
Luo Y, Xu X (2019) Predicting the helpfulness of online restaurant reviews using different machine learning algorithms: a case study of yelp. Sustainability 11:5254. https://doi.org/10.3390/SU11195254
Jo Y, Oh A (2011) Aspect and sentiment unification model for online review analysis. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining, WSDM 2011 815–824. https://doi.org/10.1145/1935826.1935932
Mei Q, Ling X, Wondra M, et al (2007) Topic sentiment mixture: modeling facets and opinions in weblogs. In: 16th International World Wide Web Conference, WWW2007 171–180. https://doi.org/10.1145/1242572.1242596
Acknowledgements
Not Applicable.
Funding
No funding was received to assist with the preparation of this manuscript.
Author information
Authors and Affiliations
Contributions
All authors contributed equally in this manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors state that they have no known competing financial interests or personal ties that could have appeared to affect the work reported in this study.
Consent to participate
Not Applicable.
Human and animal ethics
No humans or animals were harmed in any way.
Consent for publication
Not applicable.
Credit authorship contribution statement
All authors contributed equally to this study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Punetha, N., Jain, G. Aspect and orientation-based sentiment analysis of customer feedback using mathematical optimization models. Knowl Inf Syst 65, 2731–2760 (2023). https://doi.org/10.1007/s10115-023-01848-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10115-023-01848-z