Uniwersyteckie Czasopisma NaukoweIMPORTANCE OF EMOTIONS IN ADVERTISING: ASSESSMENT OF DIFFERENCES IN EMOTION LEVELS BETWEEN ADVERTISING TEXT CREATED BY COPYWRITERS AND AI IN THE PHARMACEUTICAL INDUSTRY
Słowa kluczowe:
emotions, artificial intelligence, text generators, radio adsAbstrakt
Emotions are an important element of advertising and have a decisive influence on its effectiveness. The aim of the presented article is to assess the differences in the level of emotions present in advertising texts created by a copywriter and a text generator based on artificial intelligence (AI). Sentiment analysis performed through the IBM Watson NLU application was used to assess emotions. This study provides information on the emotions present in radio advertisements for pharmaceuticals, and enables the identification of differences between human and AI generated text sources. The results show that ads from both sources have a similar emotional level, with the exception of the category "disgust", which in this situation can be used as an indicator of the origin of the ads. The results described refer only to radio advertisements for pharmaceuticals. At the end of the article, recommendations for business are formulated and directions for future research are indicated.
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Bibliografia
Chingching Ch. (2014). Guilt Regulation: The Relative Effects of Altruistic Versus Egoistic Appeals for Charity Advertising, Journal of Advertising, 43(3), pp. 211-227, DOI: 10.1080/00913367.2013.853632
Cloud.ibm.com (2022). IBM Cloud Docs. [online] Available at: https://cloud.ibm.com/docs/tone-analyzer/science,html#the-science-behind-the-service, [Accessed 10 June 2022]
Cloud.ibm.com (2022). Natural Language Understanding - IBM Cloud API Docs. [online] Available at: https://cloud.ibm.com/apidocs/natural-language-understanding?code=python#analyze [Accessed 10 June 2022]
Ermakova T., Henke M.,Fabian B., (2021). Commercial Sentiment Analysis Solutions: A Comparative Study, Proceedings of the 17th International Conference on Web Infor-mation Systems and Technologies - WEBIST, pp, 103-114, DOI:10.5220/0010709400003058
Falkowski A., Tyszka T. (2009). Psychologia zachowań konsumenckich. Gdańskie Wydaw-nictwo Psychologiczne, Gdańsk
Farnadi G., Sitaraman, G., Rohani M., Kosinski M., Stillwell D., Moens M-F., Davalos S, De Cock M. (2014). How are you doing? Emotions and Personality in Facebook. CEUR Workshop Proceedings, 1181
Global World Index, (2020). Coronavirus Research, Series 4: Media Consumption and Sport. Available at: https://www.gwi.com/hubfs/1.%20Coronavirus%20Research%20PDFs/GWI%20coronavi-rus%20findings%20April%202020%20%20Media%20Consumption%20(Release%204).pdf [Accessed 10 June 2022]
Kusumasondjaja, S., Tjiptono, F., (2019). Endorsement and visual complexity in food adver-tising on Instagram, Internet Research, DOI:29.10.1108/IntR-11-2017-0459
Matz, S. C., Kosinski, M., Nave, G., Stillwell, D. J. (2017). Psychological targeting as an effective approach to digital mass persuasion. Proceedings of the National Academy of Sciences, 114(48), 12714-12719. DOI:10.1073/pnas.1710966114
Mizerski R.W., White J. D., (1986). Understanding and using emotions in advertis-ing. Journal of Consumer Marketing, 3(4), 57-69, DOI:10.1108/eb008180
Mohammad S., Turney P., (2013). NRC emotion lexicon. DOI:10.4224/21270984
O’Shaughnessy J., O’Shaughnessy N.J. (2002). The marketing power of emotion. Oxford University Press,
Pandharinath P.H., Parekh A., Tejaswini R., Patil P., Ospanova A. (2022). Sentiment Analysis of Text Feedback. International Journal of Innovative Research in Science Engineering and Technology, 1.
Rossiter J.R., Thornton J. (2004). Fear-pattern analysis supports the fear-drive model for antispeeding road-safety TV ads. Psychology & Marketing, 21(11), pp. 945-960. DOI: 10.1002/mar.20042
Rucker, D.D., Petty, R.E. (2004). Emotion Specificity and Consumer Behavior: Anger, Sad-ness, and Preference for Activity. Motivation and Emotion 28, pp. 3–21, DOI: 10.1023/B:MOEM.0000027275.95071.82
Sánchez-Núñez P., Cobo M. J., De Las Heras-Pedrosa C., Peláez J. I., Herrera-Viedma E. (2020), Opinion Mining, Sentiment Analysis and Emotion Understanding in Advertis-ing: A Bibliometric Analysis, Journal & Magazines, IEEE Access, vol. 8, pp. 134563-134576, DOI: 10.1109/ACCESS.2020.3009482
Taylor R.K. (2000). Marketing strategies: Gaining a competitive advantage through the use of emotion. Competitiveness Review: An International Business Journal, DOI:10,1108/eb046407
