Exploring Customer Review of Local Agriculture Product Acceptance in Malaysia: A Concept Paper on Sentiment Mining
DOI:
https://doi.org/10.31436/ijpcc.v10i1.418Keywords:
sentiment mining, preferences, acceptance, Malaysia agriculture products, online reviewAbstract
Online consumer reviews in e-commerce are one technique to gather consumer opinion and sentiment about a company's products and services. However, manual analysis is impractical due to natural language text's enormous volume and complexity. Text mining and sentiment analysis methods based on machine learning provide an opportunity to analyze data for marketing objectives by increasing sales, positive electronic word-of-mouth (e-WOM), and meeting consumer demands and wants through the enhancement of market offerings. Despite the numerous benefits of analyzing e-commerce reviews to assist a company's marketing strategy, very little research has focused on sentiment and acceptance for Malaysia’s local agriculture products due to mixed language (English-Malay language) processing challenges. This concept paper highlights the use of text mining techniques to extract valuable insights from e-commerce comments related to Malaysian local agriculture products. By leveraging text mining, the study aims to better understand consumer sentiments, preferences, and feedback regarding local products, thereby facilitating improved market analysis and decision-making processes.
References
Y. Chen and J. Xie, “Online consumer review: Word-of-mouth as a new element of marketing communication mix,” Manage. Sci., vol. 54, no. 3, pp. 477–491, 2008, doi: 10.1287/mnsc.1070.0810.
T. W. Miller, Data and text Mining. A Business Application Approach. Pearson Education LTD, 2005.
G. Liu, S. Fei, Z. Yan, C.-H. Wu, and S.-B. Tsai, “An Empirical Study on Response to Online Customer Reviews and E-Commerce Sales: From the Mobile Information System Perspective,” Hindawi Mob. Inf. Syst., p. 12, 2020, doi: 10.1155/2020/8864764.
Oberlo, “10 Online Review Statistics You Need To Know in 2021,” 2021. https://www.oberlo.com/blog/online-review-statistics.
Trustpilot, “Why do people read reviews? What our research revealed,” 2020. https://business.trustpilot.com/reviews/learn-from-customers/why-do-people-read-reviews-what-our-research-revealed.
J. Pitman, “Customer Review Trends 2022,” Brightlocal, 2022. https://www.brightlocal.com/research/local-consumer-review-survey/.
M. Kavanagh, “The Impact of Customer Reviews on Purchase Decisions,” bizrate insights, 2021. https://bizrateinsights.com/resources/shopper-survey-report-the-impact-reviews-have-on-consumers-purchase-decisions/.
GlobalWebIndex, “Commerce. GlobalWebIndex’s flagship report on the latest trends in online commerce,” 2019. https://blog.gwi.com/.
ReviewTrackers, “Online Reviews Statistics and Trends: A 2022 Report by ReviewTrackers,” 2021. https://www.reviewtrackers.com/reports/online-reviews-survey/.
P. Kotler, K. Keller, M. Brady, M. Goodman, and T. Hansen, Marketing Management, 4th Europe. Pearson UK, 2019.
U. Niepewna, “7 Reasons Why Customer Feedback Is Important To Your Business,” 2022. https://blog.startquestion.com/7-reasons-why-customer-feedback-is-important-to-your-business/.
X. Liu, M. Schuckert, and R. Law, “Utilitarianism and knowledge growth during status seeking: Evidence from text mining of online reviews,” Tour. Manag., vol. 66, pp. 38–46, 2018, doi: 10.1016/j.tourman.2017.11.005.
DOSM, “Micro, Small & Medium Enterprises (MSMEs) Performance 2021,” 2021. https://www.dosm.gov.my/portal-main/release-content/micro-small-&-medium-enterprises-msmes-performance-2021.
E. Asani, H. Vahdat-Nejad, and J. Sadri, “Restaurant recommender system based on sentiment analysis,” Mach. Learn. with Appl., vol. 6, no. July, p. 100114, 2021, doi: 10.1016/j.mlwa.2021.100114.
Q. Gan, B. H. Ferns, Y. Yu, and L. Jin, “A Text Mining and Multidimensional Sentiment Analysis of Online Restaurant Reviews,” J. Qual. Assur. Hosp. Tour., vol. 18, no. 4, pp. 465–492, 2017, doi: 10.1080/1528008X.2016.1250243.
S. (Sixue) Jia, “Motivation and satisfaction of Chinese and U.S. tourists in restaurants: A cross-cultural text mining of online reviews,” Tour. Manag., vol. 78, no. December 2019, p. 104071, 2020, doi: 10.1016/j.tourman.2019.104071.
H. Li, H. Liu, and Z. Zhang, “Online persuasion of review emotional intensity: A text mining analysis of restaurant reviews,” Int. J. Hosp. Manag., vol. 89, no. October 2018, p. 102558, 2020, doi: 10.1016/j.ijhm.2020.102558.
H. Zhao, Z. Liu, X. Yao, and Q. Yang, “A machine learning-based sentiment analysis of online product reviews with a novel term weighting and feature selection approach,” Inf. Process. Manag., vol. 58, no. 5, p. 102656, 2021, doi: 10.1016/j.ipm.2021.102656.
F. Mujahid, S. Anwar, A. Afzal, L. Riaz, and M. Saad, “Enhanced Objective Sentimental Analysis Using Nlp Techniques,” Jnasp.Kinnaird.Edu.Pk, vol. 2, no. 1, pp. 217–231, 2020, [Online]. Available: http://jnasp.kinnaird.edu.pk/wp-content/uploads/2020/06/1.-Laiba-Riaz-JNASP-217-231.pdf.
R. Revathy, “A Hybrid Approach for Product Reviews Using Sentiment Analysis,” vol. 9, no. 2, pp. 340–343, 2020.
T. Willianto and A. Wibowo, “Sentiment Analysis on E-commerce Product using Machine Learning and Combination of TF-IDF and Backward Elimination,” Int. J. Recent Technol. Eng., vol. 8, no. 6, pp. 2862–2867, 2020, doi: 10.35940/ijrte.f7889.038620.
W. P. Sari and H. Fahmi, “Opinion mining analysis on online product reviews using naïve bayes and feature selection,” Proc. 2021 Int. Conf. Inf. Manag. Technol. ICIMTech 2021, no. August, pp. 256–260, 2021, doi: 10.1109/ICIMTech53080.2021.9535081.
N. Al-Twairesh and H. Al-Negheimish, “Surface and deep features ensemble for sentiment analysis of Arabic tweets,” IEEE Access, vol. 7, pp. 84122–84131, 2019, doi: 10.1109/ACCESS.2019.2924314.
M. Alassaf and A. M. Qamar, “Improving Sentiment Analysis of Arabic Tweets by One-way ANOVA,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 6, pp. 2849–2859, 2022, doi: 10.1016/j.jksuci.2020.10.023.
Malak Aljabri et al., “Sentiment Analysis of Arabic Tweets Regarding Distance Learning in Saudi Arabia during the COVID-19 Pandemic,” 2021.
B. Brahimi, M. Touahria, and A. Tari, “Improving sentiment analysis in Arabic: A combined approach,” J. King Saud Univ. - Comput. Inf. Sci., vol. 33, no. 10, pp. 1242–1250, 2021, doi: 10.1016/j.jksuci.2019.07.011.
M. Y. Khan and K. N. Junejo, “Exerting 2D-space of sentiment lexicons with machine learning techniques: A hybrid approach for sentiment analysis,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 6, pp. 599–608, 2020, doi: 10.14569/IJACSA.2020.0110672.
V. Barriere and A. Balahur, “Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation,” pp. 266–271, 2021, doi: 10.18653/v1/2020.coling-main.23.
M. Haselmayer, “Candidates rather than context shape campaign sentiment in French Presidential Elections (1965–2017).” 2021.
S. S. Jia, “Behind the ratings: Text mining of restaurant customers’ online reviews,” Int. J. Mark. Res., vol. 60, no. 6, pp. 561–572, 2018, doi: 10.1177/1470785317752048.
S. (Sixue) Jia, “Motivation and satisfaction of Chinese and U.S. tourists in restaurants: A cross-cultural text mining of online reviews,” Tour. Manag., vol. 78, no. January, p. 104071, 2020, doi: 10.1016/j.tourman.2019.104071.
L. Yu and X. Bai, “Implicit Aspect Extraction from Online Clothing Reviews with Fine-tuning BERT Algorithm,” J. Phys. Conf. Ser., vol. 1995, no. 1, 2021, doi: 10.1088/1742-6596/1995/1/012040.
A. Porreca, F. Scozzari, and M. Di Nicola, “Using text mining and sentiment analysis to analyse YouTube Italian videos concerning vaccination,” BMC Public Health, vol. 20, no. 1, pp. 1–9, 2020, doi: 10.1186/s12889-020-8342-4.
D. Contreras, S. Wilkinson, N. Balan, and P. James, “Assessing post-disaster recovery using sentiment analysis: The case of L’Aquila, Italy,” Earthq. Spectra, vol. 38, no. 1, pp. 81–108, 2022, doi: 10.1177/87552930211036486.
F. Bianchi, D. Nozza, and D. Hovy, “FEEL-IT: Emotion and Sentiment Classification for the Italian Language,” Proc. 11th Work. Comput. Approaches to Subj. Sentim. Soc. Media Anal., pp. 76–83, 2021, [Online]. Available: http://vectors.nlpl.eu/repository/20/.
B. Lauterborn, “New Marketing Litany: Four P’s Passe: C-Words Take Over. Advertising Age,” 1990.
W. G. Sumner, Folkways: A study of the sociological importance of usages, manners, customs, mores, and morals. Boston: Ginn and Co, 1906.
E. J. McCarthy, Basic Marketing: A Managerial Approach, 2nd Editio. New York: Irwin, 1964.
B. Bizumic, “Who coined the concept of ethnocentrism A brief report,” J. Soc. Polit. Psychol., vol. 2, no. 1, pp. 3–10, 2014, doi: 10.5964/jspp.v2i1.264.
E. Dichter, How Word-of-Mouth Advertising Works, Vol.44(6). Harvard business review, 1966.
T. Henning-Thurau, “Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet?,” pp. 171–193, 2004.
Ö. Ergüt, “Analysis of Online Hotel Reviews During the COVID-19 Pandemic Using Topic Modeling,” 2021, pp. 478–494.
R. Baboolal-Frank, “Analysis Of Amazon: Customer Centric Approach,” Acad. Strateg. Manag. J., vol. 20, no. SpecialIssue2, pp. 1–16, 2021.
Z. M. Sadq, S. Nuraddin, and S. Hama, “Analyzing the Amazon success strategies,” J. Process Manag. New Technol., vol. 6, no. 4, pp. 65–69, 2018, doi: 10.5937/jouproman6-19264.
A. Singh, M. Jenamani, J. J. Thakkar, and N. P. Rana, “Propagation of online consumer perceived negativity: Quantifying the effect of supply chain underperformance on passenger car sales,” J. Bus. Res., vol. 132, no. April, pp. 102–114, 2021, doi: 10.1016/j.jbusres.2021.04.027.
Y. Meng, H. Wang, and L. Zheng, “Impact of online word-of-mouth on sales?: the moderating role of product review quality,” New Rev. Hypermedia Multimed., vol. 0, no. 0, pp. 1–27, 2018, doi: 10.1080/13614568.2018.1460403.
S. P. Eslami and M. Ghasemaghaei, “Effects of online review positiveness and review score inconsistency on sales: A comparison by product involvement,” J. Retail. Consum. Serv., vol. 45, no. March, pp. 74–80, 2018, doi: 10.1016/j.jretconser.2018.08.003.
L. Cheng and C. Huang, “Exploring contextual factors from consumer reviews affecting movie sales an opinion mining approach.” Springer Nature 2019 Abstract, 2019.
A. Mishra and S. M. Satish, “eWOM: Extant Research Review and Future Research Avenues,” Vikalpa, vol. 41, no. 3, pp. 222–233, 2016, doi: 10.1177/0256090916650952.
P. Zhao, J. Wu, Z. Hua, and S. Fang, “Finding eWOM customers from customer reviews,” Ind. Manag. Data Syst., vol. 119, no. 1, pp. 129–147, 2019, doi: 10.1108/IMDS-09-2017-0418.
Q. Ben Liu and E. Karahanna, “The Dark Side Of Reviews: The Swaying Effects Of Online Product Reviews On Attribute Preference Construction,” vol. 41, no. 2, pp. 427–448, 2017.
J. E. Phelps, R. Lewis, L. Mobilio, D. Perry, and N. Raman, “Viral Marketing or Electronic Word-of-Mouth Advertising: Examining Consumer Responses and Motivations to Pass Along Email,” Cambridge Univ. Press, vol. Volume 44, no. Issue 4, pp. 333–348, 2005.
N. Colmekcioglu, R. Marvi, P. Foroudi, and F. Okumus, “Generation, susceptibility, and response regarding negativity: An in-depth analysis on negative online reviews,” J. Bus. Res., vol. 153, no. January, pp. 235–250, 2022, doi: 10.1016/j.jbusres.2022.08.033.
D. Yin, S. Mitra, and H. Zhang, “When do consumers value positive vs. negative reviews? An empirical investigation of confirmation bias in online word of mouth,” Inf. Syst. Res., vol. 27, no. 1, pp. 131–144, 2016, doi: 10.1287/isre.2015.0617.
U. Chakraborty and S. Bhat, “Credibility of online reviews and its impact on brand image,” Emerald Insight, 2018, doi: 10.1108/MRR-06-2017-0173.
M.-J. Thomas, B. W. Wirtz, and J. C. Weyerer, “Determinants Of Online Review Credibility And Its Impact On Consumers’ Purchase Intention,” vol. 20, no. 1, pp. 1–20, 2019.
L. Kwok, Y. Tang, and B. Yu, “The 7 Ps marketing mix of home-sharing services: Mining travelers’ online reviews on Airbnb,” Int. J. Hosp. Manag., vol. 90, no. August 2019, p. 102616, 2020, doi: 10.1016/j.ijhm.2020.102616.
A. Ahani et al., “Revealing customers ’ satisfaction and preferences through online review analysis?: The case of Canary Islands hotels,” J. Retail. Consum. Serv., vol. 51, no. June, pp. 331–343, 2019, doi: 10.1016/j.jretconser.2019.06.014.
P. T. Loo and R. Leung, “A service failure framework of hotels in Taiwan: Adaptation of 7Ps marketing mix elements,” J. Vacat. Mark., vol. 24, no. 1, pp. 79–100, 2018, doi: 10.1177/1356766716682555.
M. Lee, M. Jeong, and J. Lee, “Roles of negative emotions in customers’ perceived helpfulness of hotel reviews on a user-generated review website: A text mining approach,” Int. J. Contemp. Hosp. Manag., vol. 29, no. 2, pp. 762–783, 2017, doi: 10.1108/IJCHM-10-2015-0626.
K. Berezina, A. Bilgihan, C. Cobanoglu, and F. Okumus, “Understanding Satisfied and Dissatisfied Hotel Customers: Text Mining of Online Hotel Reviews,” J. Hosp. Mark. Manag., vol. 25, no. 1, pp. 1–24, 2016, doi: 10.1080/19368623.2015.983631.
F. Situmeang, N. de Boer, and A. Zhang, “Looking beyond the stars: A description of text mining technique to extract latent dimensions from online product reviews,” Int. J. Mark. Res., vol. 62, no. 2, pp. 195–215, 2019, doi: 10.1177/1470785319863619.
V. R. Hananto, S. Kim, M. Kovacs, U. Serdult, and V. Kryssanov, “A Machine Learning Approach to Analyze Fashion Styles from Large Collections of Online Customer Reviews,” 6th Int. Conf. Bus. Ind. Res. ICBIR 2021 - Proc., no. Icbir, pp. 153–158, 2021, doi: 10.1109/ICBIR52339.2021.9465830.
Y. Zhou, S. Yang, Y. Li, Y. Chen, J. Yao, and A. Qazi, “Does the review deserve more helpfulness when its title resembles the content? Locating helpful reviews by text mining,” Inf. Process. Manag., vol. 57, no. 2, p. 102179, 2020, doi: 10.1016/j.ipm.2019.102179.
N. Vemprala, R. R. Xiong, C. Z. Liu, and K. R. Choo, “Where Does My Product Stand?? A Social Network Perspective on Online Product Reviews,” Proc. 52nd Hawaii Int. Conf. Syst. Sci., vol. 6, pp. 2345–2354, 2019.
W. Wang, Y. Feng, and W. Dai, “Topic analysis of online reviews for two competitive products using latent Dirichlet allocation,” Electron. Commer. Res. Appl., vol. 29, no. January, pp. 142–156, 2018, doi: 10.1016/j.elerap.2018.04.003.
H. Li, Q. Chen, Z. Zhong, R. Gong, and G. Han, “E-word of mouth sentiment analysis for user behavior studies,” Inf. Process. Manag., vol. 59, no. 1, p. 102784, 2022, doi: 10.1016/j.ipm.2021.102784.
J. Li, G. Li, M. Liu, X. Zhu, and L. Wei, “A novel text-based framework for forecasting agricultural futures using massive online news headlines,” Int. J. Forecast., vol. 38, no. 1, pp. 35–50, 2022, doi: 10.1016/j.ijforecast.2020.02.002.
R. Pradipta and R. Jayadi, “the Sentiment Analysis of the Indonesian Palm Oil Industry in Social Media Using a Machine Learning Model,” J. Theor. Appl. Inf. Technol., vol. 100, no. 12, pp. 4532–4542, 2022.
J. Xu and Y.-L. Hsu, “The Impact of News Sentiment Indicators on Agricultural,” Comput. Econmics, vol. 59, pp. 1645–1657, 2022.
S. Yadav, A. Kaushik, M. Sharma, and S. Sharma, “Disruptive Technologies in Smart Farming: An Expanded View with Sentiment Analysis,” AgriEngineering, vol. 4, no. 2, pp. 424–460, 2022, doi: 10.3390/agriengineering4020029.
S. Jiang, R. Angarita, S. Cormier, J. Orensanz, and F. Rousseaux, “Informativeness in Twitter Textual Contents for Farmer-centric Plant Health Monitoring,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 13364 LNCS, pp. 492–503, 2022, doi: 10.1007/978-3-031-09282-4_41.
M. Ofori and O. El-Gayar, “Drivers and challenges of precision agriculture: a social media perspective,” Precis. Agric., vol. 22, no. 3, pp. 1019–1044, 2021, doi: 10.1007/s11119-020-09760-0.
J. N. Salim, D. Trisnawarman, and M. C. Imam, “Twitter users opinion classification of smart farming in Indonesia,” IOP Conf. Ser. Mater. Sci. Eng., vol. 852, no. 1, 2020, doi: 10.1088/1757-899X/852/1/012165.
M. Ofori and O. El-Gayar, “The State and Future of Smart Agriculture: Insights from mining social media,” Proc. - 2019 IEEE Int. Conf. Big Data, Big Data 2019, pp. 5152–5161, 2019, doi: 10.1109/BigData47090.2019.9006587.
D. Tiwari and B. Nagpal, KEAHT: A Knowledge-Enriched Attention-Based Hybrid Transformer Model for Social Sentiment Analysis, no. 0123456789. Ohmsha, 2022.
A. Kumaravel, G. Ayyappan, T. Vijayan, and K. Alice, “Trails with ensembles on sentimental sensitive data for agricultural twitter exchanges,” Indian J. Comput. Sci. Eng., vol. 12, no. 5, pp. 1372–1381, 2021, doi: 10.21817/INDJCSE/2021/V12I5/211205073.
M. Rehman et al., “Semantics Analysis of Agricultural Experts’ Opinions for Crop Productivity through Machine Learning,” Appl. Artif. Intell., vol. 36, no. 1, 2022, doi: 10.1080/08839514.2021.2012055.
S. Evanega, J. Conrow, J. Adams, and M. Lynas, “The state of the ‘GMO’ debate - toward an increasingly favorable and less polarized media conversation on ag-biotech?,” GM Crop. Food, vol. 13, no. 1, pp. 38–49, 2022, doi: 10.1080/21645698.2022.2051243.
A. Corallo, L. Fortunato, A. Spennato, F. Errico, and A. Pedone, “Predicting the Consumer’s Purchase Intention of Food Products,” ICITM 2020 - 2020 9th Int. Conf. Ind. Technol. Manag., pp. 181–185, 2020, doi: 10.1109/ICITM48982.2020.9080404.
M. F. M. Mohsin, S. S. Kamaruddin, F. Siraj, H. A. Hambali, and M. A. Taiye, “Investigating the relevant agro food keyword in Malaysian online newspapers,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. 6, pp. 2166–2175, 2019, doi: 10.18517/ijaseit.9.6.7955.
M. M. Mostafa, “Mining and mapping halal food consumers: A geo-located Twitter opinion polarity analysis,” J. Food Prod. Mark., vol. 24, no. 7, pp. 858–879, 2018, doi: 10.1080/10454446.2017.1418695.
B. Drury and M. Roche, “A survey of the applications of text mining for agriculture,” Comput. Electron. Agric., vol. 163, no. February, p. 104864, 2019, doi: 10.1016/j.compag.2019.104864.
P. Nimirthi, P. Venkata Krishna, M. S. Obaidat, and V. Saritha, “A framework for sentiment analysis based recommender system for agriculture using deep learning approach,” SpringerBriefs Appl. Sci. Technol., pp. 59–66, 2019, doi: 10.1007/978-981-13-1456-8_5.
O. Bermeo-Almeida, J. del Cioppo-Morstadt, M. Cardenas-Rodriguez, R. Cabezas-Cabezas, and W. Bazán-Vera, “Sentiment Analysis in Social Networks for Agricultural Pests,” Adv. Intell. Syst. Comput., vol. 901, pp. 122–129, 2019, doi: 10.1007/978-3-030-10728-4_13.
S. Zhang, “Sentiment analysis based on food e-commerce reviews,” IOP Conf. Ser. Earth Environ. Sci., vol. 792, no. 1, 2021, doi: 10.1088/1755-1315/792/1/012023.
Y. H. Li, J. Zheng, Z. P. Fan, and L. Wang, “Sentiment analysis-based method for matching creative agri-product scheme demanders and suppliers: A case study from China,” Comput. Electron. Agric., vol. 186, no. April, p. 106196, 2021, doi: 10.1016/j.compag.2021.106196.
Q. Tao, Z. Wang, C. Gu, Y. Zhan, J. Xu, and Z. Tang, “Intelligent optimal lifecycle planning in agricultural products supply chains using cloud computing and RFID data,” ICNC-FSKD 2017 - 13th Int. Conf. Nat. Comput. Fuzzy Syst. Knowl. Discov., pp. 66–71, 2018, doi: 10.1109/FSKD.2017.8393349.
N. Kewsuwun and S. Kajornkasirat, “A sentiment analysis model of agritech startup on Facebook comments using naive Bayes classifier,” Int. J. Electr. Comput. Eng., vol. 12, no. 3, pp. 2829–2838, 2022, doi: 10.11591/ijece.v12i3.pp2829-2838.
K. Kosior, “Social Media Analytics in Food Innovation and Production?: a Review,” Proc. Food Syst. Dyn., vol. 0, no. 0, pp. 205–219, 2019, [Online]. Available: www.centmapress.org.
S. D. Juventia, S. K. Jones, M. A. Laporte, R. Remans, C. Villani, and N. Estrada-Carmona, “Text mining national commitments towards agrobiodiversity conservation and use,” Sustain., vol. 12, no. 2, 2020, doi: 10.3390/su12020715.
R. Pruthvi, V. S., & Naik, “Prediction based Policy setting by finding significance of Attributes from the Ontological Framework in Agricultural domain,” 2018 IEEE Symp. Ser. Comput. Intell., pp. 1937–1940, 2018.
A. Kumar and A. Sharma, “Socio-Sentic framework for sustainable agricultural governance,” Sustain. Comput. Informatics Syst., vol. 28, p. 100274, 2020, doi: 10.1016/j.suscom.2018.08.006.
E. M. Akhmetshin and A. V. Plotnikov, “Sentiment analysis of client reviews of the Russian Agricultural Bank service and predicted rating reviews,” IOP Conf. Ser. Earth Environ. Sci., vol. 548, no. 2, 2020, doi: 10.1088/1755-1315/548/2/022042.
Y. Cao, Z. Sun, L. Li, and W. Mo, “A Study of Sentiment Analysis Algorithms for Agricultural Product Reviews Based on Improved BERT Model,” Symmetry (Basel)., vol. 14, no. 8, p. 1604, 2022, doi: 10.3390/sym14081604.
H. Zikang, Y. Yong, Y. Guofeng, and Z. Xinyu, “Sentiment analysis of agricultural product ecommerce review data based on deep learning,” 2020 Int. Conf. Internet Things Intell. Appl. ITIA 2020, 2020, doi: 10.1109/ITIA50152.2020.9312251.
A. Vaswani et al., “Attention Is All You Need,” 31st Conf. Neural Inf. Process. Syst. (NIPS 2017, 2017, doi: https://doi.org/10.48550/arXiv.1706.03762.
J. Devlin and M.-W. Chang, “Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing,” Google AI Blog, 2018. https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” 2019, doi: https://doi.org/10.48550/arXiv.1810.04805.
Agrobazaar, “Agrobazaar Online,” Agrobazaar Online, 2022. https://www.agrobazaar.com.my/web/.
