Have a personal or library account? Click to login
Evaluation of Cluster Management Quality Based on Consumer Opinion Sentiment Analysis Cover

Evaluation of Cluster Management Quality Based on Consumer Opinion Sentiment Analysis

Open Access
|Dec 2021

References

  1. Abirami, A.M., Askarunisa, A. 2017. Sentiment analysis model to emphasize the impact of online reviews in healthcare industry. Online Information Review, 41(4), pp.471–486.
  2. Albesta, D.D., Jonathan, M.L., Jawad, M., Hardiawan, O., Suhartono, D. 2021. The impact of sentiment analysis from user on Facebook to enhanced the service quality. International Journal of Electrical & Computer Engineering (2088–8708), 11(4), pp.3424–3433.
  3. Archak, N., Ghose, A., Ipeirotis, P.G., 2011. Deriving the pricing power of product features by mining consumer reviews. Management Science, 57(8), pp.1485–1509.
  4. Asur, S., Huberman, B.A., 2010. Predicting the future with social media. In 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Vol. 1, IEEE., pp.492–499.
  5. Bernatowicz, A., Małyszko, J., 2014. Recenzje konsumenckie w Internecie. Społeczny kontekst publikowania opinii i analiza spójności różnych sposobów ich wyrażania (Consumer Reviews on the Internet. The Social Context of Opinion Publishing and an Analysis of the Consistency of Different ways of Expressing them), In: D. Appenzeller (ed.), 2014. Matematyka i informatyka na usługach ekonomii: teoria i zastosowania (Mathematics and Computer Science at the Service of Economics: Theory and Applications). Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu (Published by the University of Economics in Poznań), pp.158–168.
  6. Bruhn, M., Hennig-Thurau, T., Hadwich, K., 2004. Markenführung und Relationship Marketing. In: Handbuch Markenführung. Gabler Verlag, Wiesbaden, pp.391–420.
  7. Chaney, P., 2020. 26 Business Review Sites Where Customers Rate You. Small Business TRENDS, [online] Available at: https://smallbiz-trends.com/2020/12/business-review-sites.html [Access: 08.06.2021]
  8. Chen, B., Zhu, L., Kifer, D., Lee, D., 2010. What is an Opinion About? Exploring Political Standpoints Using Opinion Scoring Model. Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, pp.1007–1012. https://dl.acm.org/doi/abs/10.5555/2898607.2898768.
  9. Chong, A.Y.L., Li, B., Ngai, E.W.T., Ch'ng, E., Lee, F., 2016. Predicting online product sales via online reviews, sentiments, and promotion strategies: A big data architecture and neural network approach. International Journal of Operations & Production Management. Vol. 36, No. 4, pp.358–383. https://doi.org/10.1108/IJOPM-03-2015-0151
  10. Das, S., Chen, M., 2001. Yahoo! For Amazon: Extracting Market Sentiment from Stock Message Boards. In: Proceedings of the Asia Pacific Finance Association Annual Conference (APFA) Vol. 35, p.4–45.
  11. Dbohra, 2021. 15 Business Review Sites Where Customers Rate You, Branding, Business, [online] Available at: https://dbohra.com/blog/index.php/2021/01/13/15-business-review-sites-customers-rate-you/ [Access:08.06.2021]
  12. Directorate For Science, Technology And Innovation Committee On Consumer Policy, 2018. Understanding Online Consumer Ratings And Reviews, Organisation for Economic Co-operation and Development, pp.1–25. [online] Available at: https://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=DSTI/CP(2018)21/FINAL&docLanguage=En [Access: 14.08.2021].
  13. Duan, W., Cao, Q., Yu, Y., Levy, S., 2013. Mining Online User-Generated Content: Using Sentiment Analysis Technique to Study Hotel Service Quality. In 2013 46th Hawaii International Conference on System Sciences (IEEE.), pp.3119–3128.
  14. Dzieciątko, M., 2018. Application of Text Analytics to Analyze Emotions in the Speeches. In International Conference on Information Technologies in Biomedicine, Springer, Cham, pp.525–536.
  15. Gimpel, K., Schneider, N., O’Connor, B., et al., 2010. Part-of-speech Tagging for Twitter: Annotation, Features, and Experiments. Carnegie-Mellon Univ Pittsburgh Pa School of Computer Science.
  16. Gładysz, A., 2017. Analiza wydźwięku polskojęzycznych opinii konsumenckich: implementacja algorytmu tworzenia słownika wydźwięku. Overtone Analysis of Polish-language Consumer Opinions: Implementation of an Algorithm for Creating an Overtone Dictionary). Autobusy: technika, eksploatacja, systemy transportowe (Buses: technique, operation, transport systems), Vol. 12, p.1692–1697.
  17. Guta, M., 2017. 97% of Customers Read Online Reviews, Survey Says. Small Business Trends. [online] Available at: https://smallbiz-trends.com/2017/11/2017-local-consumer-review-survey.html [Access: 02.08.2021]
  18. Guttmann, A., 2017. Most Trusted Sources for Product Information in the U.S. in 2016, Advertising & Marketing, [online] Available at: https://www.statista.com/statistics/251456/content-online-shoppers-trust-when-researching-products-in-the-us/ [Access 05.08.2021]
  19. Jeyapriya, A., Selvi, C.K., 2015. Extracting Aspects and Mining Opinions in Product Reviews Using Supervised Learning Algorithm. Proceedings of the 2nd International Conference on Electronics and Communication Systems (ICECS), IEEE, pp.548–552.
  20. Kantar Media, 2019. What Brand Information Sources Do People Trust the Most?. MarketingCharts. [online] Available at: https://www.marketingcharts.com/brand-related-108281 [Access 05.08.2021].
  21. Kauffmann, E., Peral, J., Gil, D., Ferrández, A., Sellers, R., Mora, H. 2020. A Framework for Big Data Analytics in Commercial Social Networks: A Case Study on Sentiment Analysis and Fake Review Detection for Marketing Decision-Making. Industrial Marketing Management, 90, pp.523–537.
  22. Liu, B. 2011. Opinion Mining and Sentiment Analysis. In: B. Liu (ed.), 2011. Web Data Mining. Springer, Berlin, Heidelberg, pp.459–526.
  23. Liu, B., 2015. Sentiment Analysis, Cambridge University Press, pp.259–301.
  24. Liu, Y., Huang, X., An, A., Yu, X. 2007. ARSA: A Sentiment-aware Model for Predicting Sales Performance Using Blogs. Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 607–614.
  25. Małyszko, J., 2015. Automatyczne przetwarzanie recenzji konsumenckich dla oceny użyteczności produktów i usług (Automatic Processing of Consumer Reviews for Usability Evaluation of Products and Services). Rozprawa doktorska (PhD thesis), doctoral supervisor: Abramowicz, W., Poznań University of Economics and Business, p. 16–155.
  26. Mandel, A., 2018. Producent Doritos ujawnia – olej palmowy zniknie ze składu przekąsek (Doritos Manufacturer Reveals - Palm Oil Will Disappear from Snack Ingredients), Rzeczpospolita, [online] Available at: https://www.rp.pl/Przemysl-spozywczy/180909587-Producent-Doritos-ujawnia--olej-palmowy-zniknie-ze-skladu-przekasek.html [Access: 14.10.2018].
  27. McGlohon, M., Glance, N., Reiter, Z., 2010. Star Quality: Aggregating Reviews to Rank Products and Merchants. Proceedings of Fourth international AAAI Conference on Weblogs and Social Media, pp.114–121.
  28. Młodzianowski, P., 2018. A Study of the Influence of Online Information on the Changes in the Warsaw Stock Exchange Indexes. Acta Universitatis Lodziensis, Vol. 335, No 3, pp.123–138.
  29. Nakayama, M., Wan, Y., 2019. The Cultural Impact on Social commerce: A Sentiment Analysis on Yelp Ethnic Restaurant Reviews. Information & Management, 56(2), pp. 271–279.
  30. Nasukawa, T., Yi, J., 2003. Sentiment Analysis: Capturing Favorability Using Natural Language Processing. In Proceedings of the 2nd international conference on Knowledge capture, pp. 70–77.
  31. Pang, B., Lee, L., Vaithyanathan, S., 2002. Thumbs up? Sentiment classification using machine learning techniques. arXiv:cs/0205070.
  32. Perkins, B., Fenech, C., 2016. The Deloitte Consumer Review The Growing Power of Consumers, A Deloitte Insight report, [online] Available at: https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/consumer-business/consumer-review-8-the-growing-power-of-consumers.pdf [Access: 11.08.2021]
  33. Pozzi, F. A., Fersini, E., Messina, E., Liu, B., 2016. Sentiment Analysis in Social Networks. Morgan Kaufmann, p.18–21.
  34. Ravi, K., Ravi, V., 2015. A Survey on Opinion Mining and Sentiment Analysis: Tasks, Approaches and Applications. Knowledge based systems, 89, pp.14–46.
  35. Reviewtrackers, 2021. 19 Business Review Sites for Improving Your Brand Visibility. Reviewtrackers, [online] Available at: https://www.reviewtrackers.com/guides/business-review-sites/ [Access: 08.06.2021]
  36. Sadikov, E., Parameswaran, A., Venetis, P. 2009. Blogs as predictors of movie success. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 3, No. 1.
  37. Taylor, K., 2018. Reklama piwa Heineken wycofana. Po oskarżeniach o rasizm (Heineken Beer ad Withdrawn. After Accusations of Racism), BUSINESS INSIDER, [online] Available at: https://businessinsider.com.pl/media/reklama/heineken-wycofal-reklame-po-oskrazeniach-o-rasizm/38l6prn [Access: 14.10.2018]
  38. Tumasjan, A., Sprenger, T., Sandner, P., Welpe, I., 2010. Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 4, No. 1.
  39. Qazi, A., Tamjidyamcholo, A., Raj, R.G., Hardaker, G., Standing, C., 2017. Assessing Consumers’ Satisfaction and Expectations through Online Opinions: Expectation and Disconfirmation Approach. Computers in Human Behavior, 75, pp.450–460.
DOI: https://doi.org/10.2478/fman-2021-0017 | Journal eISSN: 2300-5661 | Journal ISSN: 2080-7279
Language: English
Page range: 219 - 228
Published on: Dec 28, 2021
Published by: Warsaw University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2021 Piotr Młodzianowski, Jose Aldo Valencia Hernandez, published by Warsaw University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.