Analysis Sentiment of Nestle Bear Brand during the Covid-19 Pandemic on Social Media Twitter
Main Article Content
Position and brand image are important and crucial in marketing strategy. Brand position and image form strong associations with targeted consumers used to differentiate a brand from its competitors. Companies must understand good marketing strategies to improve their brand position in society based on consumer perspective. During the Covid-19 pandemic, especially in 2021, there was a unique phenomenon, whereby the demand for Bear Brand Milk was greater than the market leader, namely Ultra milk, while Ultra milk was even cheaper than the Bear Brand milk. This phenomenon is discussed on social media, especially Twitter regarding opinions from consumers in purchasing the products. This study aims to determine whether online text reviews can provide an overview of the position and brand image of Bear Brand Milk using LIWC (Linguistic Inquiry and Word Count) sentiment analysis and PCA (Principal Component Analysis). Based on our analysis, we can conclude that LIWC can provide an overview regarding the brand image and brand positioning of Bear Brand milk and Ultra milk. Brand image is obtained from variables that describe psychological variables when using or imagining the brands. Brand Position through PCA analysis describes the difference in gain between the dominant variables in the two brands.
Keywords:
Brand Position, Brand Image, Bear Brand Milk, PCA, LIWC Sentiment Analysis
Aaker, David A. (1996), Building Strong Brands. New York: The Free Press.
Ahmed, W. (2019). Using Twitter as a data source: An overview of social media research tools (2019). Impact of Social Sciences Blog. https://blogs.lse.ac.uk/impactofsocialsciences/2019/06/18/usingtwitter-as-a-data-source-an-overview-of-social-media-research-tools-2019/
Arafat, S M Yasir & Kar, Sujita & Kabir, Russell. (2021). Editorial: Panic Buying: Human Psychology and Environmental Influence. Frontiers in Public Health. 9. 10.3389/fpubh.2021.694734.
Algharabat, R. S. (2018). The role of telepresence and user engagement in co-creation value and purchase intention: Online retail context. Journal of Internet Commerce, 17(1), 1-25. doi:10.1080/15332861.2017.1422667
Alzate M, Arce-Urriza M, Cebollada J. (2022). Mining the text of online consumer reviews to analyze brand image and brand positioning, Journal of Retailing and Consumer Services, Volume 67. 2022, 102989, ISSN 0969-6989, https://doi.org/10.1016/j.jretconser.2022.102989.
A Van Looy (2016). “Sentiment Analysis and Opinion Mining,” In Social Media Management, Springer, pp. 133–147
Boyd, R. L., & Pennebaker, J. W. (2016). A way with words: Using language for psychological science in the modern era. In C. Dimofte, C. P. Haugtvedt, & R. F. Yalch (Eds.), Consumer psychology in a social media world (pp. 222–236). Routledge/Taylor & Francis Group
Carpenter, G. S., & Nakamoto, K. (1989). Consumer Preference Formation and pioneering advantage. Journal of Marketing Research, 26(3), 285. doi:10.2307/3172901
Casalo, L.V.; Flavian, C.; Guinaliu, M.; Ekinci, Y. Do online hotel rating schemes influence booking behaviors? Int. J. Hosp. Manag. 2015,49, 28–36
Chakraborty, U., & Bhat, S. (2018). Effect of Credible Reviews on Brand Image: A Mixed Method Approach. IIM Kozhikode Society & Management Review, 7(1), 13–22. https://doi.org/ 10.1177/2277975217733873
Chong, W., Selvaretnam, B., & Soon, L. (2014). Natural Language Processing for Sentiment Analysis: An Exploratory Analysis on Tweets. 2014 4Th International Conference On Artificial Intelligence With Applications In Engineering And Technology. doi: 10.1109/icaiet.2014.43
Cunningham, John & Ghahramani, Zoubin. (2015). Linear Dimensionality Reduction: Survey, Insights, and Generalizations. J. Mach. Learn. Res. 16, 1 (January 2015), 2859–2900.
DUAN, W., GU, B., & WHINSTON, A. (2008). The dynamics of online word-of-mouth and product sales—an empirical investigation of the movie industry. Journal of Retailing, 84(2), 233-242. doi:10.1016/j.jretai.2008.04.005
Dwivedi, Y., Ismagilova, E., Hughes, D., Carlson, J., Filieri, R., & Jacobson, J. et al. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal Of Information Management, 59, 102168. doi: 10.1016/j.ijinfomgt.2020.102168
Ferguson, I. (2020). Neoliberal social work and covid-19. Social Work and the COVID-19 Pandemic, 25-30. doi:10.2307/j.ctv1850gc4.8
Hartmann, J., Huppertz, J., Schamp, C., & Heitmann, M. (2019). Comparing automated text classification methods. International Journal of Research in Marketing, 36(1), 20-38. https://doi.org/10.1016/j.ijresmar.2018.09.009
Hasibuan, L. (2021, July 04). Heboh Susu Beruang Diburu Warga, Begini Komentar Nestle! Retrieved December 28, 2022, from https://www.cnbcindonesia.com/news/20210704163551-4-258119/heboh-susu-beruang-diburu-warga-begini-komentar-nestle
Hassani, H., Beneki, C., Unger, S., Mazinani, M., & Yeganegi, M. (2021). Text Mining in Big Data Analytics. Retrieved 27 December 2022
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 38-52. doi:10.1002/dir.10073
Hewett, Kelly, William Rand, Roland T. Rust, and Harald van Heerde (2016), “Brand Buzz in the Echoverse,” Journal of Marketing, 80 (3), 1–24.
Hosea, C., & Rokhim, R. (2020). Mining Relationships among Online Review Texts and Ratings in Indonesian E-commerce Websites. Proceedings Of The 2020 The 4Th International Conference On Big Data And Internet Of Things. doi: 10.1145/3421537.3421543
Jeon, H. J., Kim, D. Y., Han, K. J., Han, D.W., Son, S. W., & Lee, C. M. (2018). An analysis of indoor environment research trends in Korea using topic modeling: Case study on abstracts from the journal of the Korean society for indoor environment. Journal of Odor and Indoor Environment,17(4), 322–329. doi:10.15250/joie.2018.17.4.322
Julie L. Locher PhD, William C. Yoels, Donna Maurer & Jillian van Ells (2005). Comfort Foods: An Exploratory Journey Into The Social and Emotional Significance of Food, Food and Foodways, 13:4, 273-297, DOI: 10.1080/07409710500334509
J. Yi, T. Nasukawa, R. Bunescu, and W. Niblack (2003) , “Sentiment analyzer: Extracting sentiments about a given topic using natural anguage processing techniques”, in Data Mining 2003, ICDM 2003. Third IEEE International Conference, pp. 427-434, IEEE, 2003.
Kassambara, A. and Mundt, F. (2020) Factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R Package Version 1.0.7.
Ahmed, W. (2019). Using Twitter as a data source: An overview of social media research tools (2019). Impact of Social Sciences Blog. https://blogs.lse.ac.uk/impactofsocialsciences/2019/06/18/usingtwitter-as-a-data-source-an-overview-of-social-media-research-tools-2019/
Arafat, S M Yasir & Kar, Sujita & Kabir, Russell. (2021). Editorial: Panic Buying: Human Psychology and Environmental Influence. Frontiers in Public Health. 9. 10.3389/fpubh.2021.694734.
Algharabat, R. S. (2018). The role of telepresence and user engagement in co-creation value and purchase intention: Online retail context. Journal of Internet Commerce, 17(1), 1-25. doi:10.1080/15332861.2017.1422667
Alzate M, Arce-Urriza M, Cebollada J. (2022). Mining the text of online consumer reviews to analyze brand image and brand positioning, Journal of Retailing and Consumer Services, Volume 67. 2022, 102989, ISSN 0969-6989, https://doi.org/10.1016/j.jretconser.2022.102989.
A Van Looy (2016). “Sentiment Analysis and Opinion Mining,” In Social Media Management, Springer, pp. 133–147
Boyd, R. L., & Pennebaker, J. W. (2016). A way with words: Using language for psychological science in the modern era. In C. Dimofte, C. P. Haugtvedt, & R. F. Yalch (Eds.), Consumer psychology in a social media world (pp. 222–236). Routledge/Taylor & Francis Group
Carpenter, G. S., & Nakamoto, K. (1989). Consumer Preference Formation and pioneering advantage. Journal of Marketing Research, 26(3), 285. doi:10.2307/3172901
Casalo, L.V.; Flavian, C.; Guinaliu, M.; Ekinci, Y. Do online hotel rating schemes influence booking behaviors? Int. J. Hosp. Manag. 2015,49, 28–36
Chakraborty, U., & Bhat, S. (2018). Effect of Credible Reviews on Brand Image: A Mixed Method Approach. IIM Kozhikode Society & Management Review, 7(1), 13–22. https://doi.org/ 10.1177/2277975217733873
Chong, W., Selvaretnam, B., & Soon, L. (2014). Natural Language Processing for Sentiment Analysis: An Exploratory Analysis on Tweets. 2014 4Th International Conference On Artificial Intelligence With Applications In Engineering And Technology. doi: 10.1109/icaiet.2014.43
Cunningham, John & Ghahramani, Zoubin. (2015). Linear Dimensionality Reduction: Survey, Insights, and Generalizations. J. Mach. Learn. Res. 16, 1 (January 2015), 2859–2900.
DUAN, W., GU, B., & WHINSTON, A. (2008). The dynamics of online word-of-mouth and product sales—an empirical investigation of the movie industry. Journal of Retailing, 84(2), 233-242. doi:10.1016/j.jretai.2008.04.005
Dwivedi, Y., Ismagilova, E., Hughes, D., Carlson, J., Filieri, R., & Jacobson, J. et al. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal Of Information Management, 59, 102168. doi: 10.1016/j.ijinfomgt.2020.102168
Ferguson, I. (2020). Neoliberal social work and covid-19. Social Work and the COVID-19 Pandemic, 25-30. doi:10.2307/j.ctv1850gc4.8
Hartmann, J., Huppertz, J., Schamp, C., & Heitmann, M. (2019). Comparing automated text classification methods. International Journal of Research in Marketing, 36(1), 20-38. https://doi.org/10.1016/j.ijresmar.2018.09.009
Hasibuan, L. (2021, July 04). Heboh Susu Beruang Diburu Warga, Begini Komentar Nestle! Retrieved December 28, 2022, from https://www.cnbcindonesia.com/news/20210704163551-4-258119/heboh-susu-beruang-diburu-warga-begini-komentar-nestle
Hassani, H., Beneki, C., Unger, S., Mazinani, M., & Yeganegi, M. (2021). Text Mining in Big Data Analytics. Retrieved 27 December 2022
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 38-52. doi:10.1002/dir.10073
Hewett, Kelly, William Rand, Roland T. Rust, and Harald van Heerde (2016), “Brand Buzz in the Echoverse,” Journal of Marketing, 80 (3), 1–24.
Hosea, C., & Rokhim, R. (2020). Mining Relationships among Online Review Texts and Ratings in Indonesian E-commerce Websites. Proceedings Of The 2020 The 4Th International Conference On Big Data And Internet Of Things. doi: 10.1145/3421537.3421543
Jeon, H. J., Kim, D. Y., Han, K. J., Han, D.W., Son, S. W., & Lee, C. M. (2018). An analysis of indoor environment research trends in Korea using topic modeling: Case study on abstracts from the journal of the Korean society for indoor environment. Journal of Odor and Indoor Environment,17(4), 322–329. doi:10.15250/joie.2018.17.4.322
Julie L. Locher PhD, William C. Yoels, Donna Maurer & Jillian van Ells (2005). Comfort Foods: An Exploratory Journey Into The Social and Emotional Significance of Food, Food and Foodways, 13:4, 273-297, DOI: 10.1080/07409710500334509
J. Yi, T. Nasukawa, R. Bunescu, and W. Niblack (2003) , “Sentiment analyzer: Extracting sentiments about a given topic using natural anguage processing techniques”, in Data Mining 2003, ICDM 2003. Third IEEE International Conference, pp. 427-434, IEEE, 2003.
Kassambara, A. and Mundt, F. (2020) Factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R Package Version 1.0.7.
