With this post, we start a series dedicated to the use of our Text Analytics technology in the field of R&D in different sectors. This post is dedicated to the use of Text Analytics in Marketing Research. First of all, we would like to thank the researchers who have selected our services (within a wide range of competitors) as a basis for their research or innovation projects.
The applications of Text Analytics in marketing are countless. That is why more and more companies are using these tools, starting with giants like Nielsen or TNS. Text analysis driven by natural language processing (NLP) is helping to transform digital marketing strategies. There are several uses for it in the company: evaluating marketing impact, optimizing customer service and SEO, making the most of influencer marketing, and significantly improving social listening. We will elaborate on this last topic in a future post.
For the time being, here is a list of references to recent scientific publications that use MeaningCloud’s Text Analytics services in Marketing applications as a basis for their work. In alphabetic order:
Arechederra, B., & Zanfardini, M. ¿Qué dicen los huéspedes que viajan por razones de negocios sobre las marcas hoteleras de la Ciudad Autónoma de Buenos Aires? Revista Argentina de Investigación en Negocios, 6(1), 113-126.
Bilro, R. G., & Loureiro, S. M. C. (2020). A consumer engagement systematic review: synthesis and research agenda. Spanish Journal of Marketing-ESIC.
Bilro, R. G., Loureiro, S. M. C., & Guerreiro, J. (2019). Exploring online customer engagement with hospitality products and its relationship with involvement, emotional states, experience and brand advocacy. Journal of Hospitality Marketing & Management, 28(2), 147-171.
Caviggioli, F., Lamberti, L., Landoni, P., & Meola, P. (2020). Technology adoption news and corporate reputation: sentiment analysis about the introduction of Bitcoin. Journal of Product & Brand Management.
Costa, I. O. M. D. (2019). The impact of influencer marketing on consumer purchase intentions and brand attitude: the Instagrammers. Doctoral dissertation, University of Lisbon.
Güneş, S. (2020). Extracting Online Product Review Patterns and Causes: A New Aspect/Cause Based Heuristic for Designers. The Design Journal, 23(3), 375-393.
Häusl, M., Forster, J., & Auch, M. (2019, September). Marcel Karrasch, and Peter Mandl. An evaluation concept for name entity recognition and keyword APIs in social media analysis. Proceedings of the IWEMB 2018, Second International Workshop on Entrepreneurship in Electronic and Mobile Business.
Loureiro, S. M. C., Bilro, R. G., & de Aires Angelino, F. J. (2020). Virtual reality and gamification in marketing higher education: a review and research agenda. Spanish Journal of Marketing-ESIC.
Montero, H. (2020). User-Experience based Evaluation of the Jaxber App. BSc. Thesis, JAMK University of Applied Sciences, Finland.
Rane, A. L., & Kshatriya, A. R. (2020). Audio Opinion Mining and Sentiment Analysis of Customer Product or Services Reviews. In ICDSMLA 2019 (pp. 282-293). Springer, Singapore.
Rosado-Pinto, F., Loureiro, S. M. C., & Bilro, R. G. (2020). How Brand Authenticity and Consumer Brand Engagement Can Be Expressed in Reviews: A Text Mining Approach. Journal of Promotion Management, 26(4), 457-480.
Wekwerth, Z. M. (2019). Responding to online firestorms on social media: an analysis of the two company cases Dolce & Gabbana and Gucci. Master’s thesis, University of Twente.
We hope you find this information useful and interesting to open the spectrum of possible usage of Text Analytics techniques in Marketing.