Abstract
Nowadays, firms have realized the importance of Big Data, highlighting the need for understanding the current state of marketing practice with respect to Big Data analytics. Among the different sources of Big Data, User-Generated Content (UGC) is one of the most important ones. From blogs to social media and online reviews, consumers generate huge amounts of brand related information that have a decisive potential business value in targeted advertising, customer engagement or brand communication, among others. In the same line, previous empirical findings show that UGC has significant effects on brand images, purchase intentions, and sales. It plays an important role for customers’ potential buying decisions. Thus, mining and analysing UGC data such as comments and sentiments might be useful for firms. Particularly, brand management can be one area of interest, as online reviews might have an influence on brand image and brand positioning. Within this context, as well as the quantitative star score usual in this UGC, in which the buyers rate the product, a recent stream of research employs Sentiment Analysis (SA) tools with the aim of examining the textual content of the review and categorizing buyers’ opinions. While certain SA split the comments into two classes (negative or positive), other incorporate more sentiment classes. However, the review can have phrases with different polarities because the user can have different experiences and sentiments about each feature of the product. Finding the polarity of each feature can be interesting for the decision makers of a product. In this paper, we consider that although these two scores (star and sentiment) are related, the sentiment score highlights extra information not detailed in the star score, which is crucial to be extracted in order to have better criteria of comparison between products. Moreover, we mine the positive and negative features of the products analysing the sentiment.
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Acknowledgements
This work was supported in part by the Spanish Ministry of Science, Innovation and Universities through the Project ECLIPSE-UA under Grant RTI2018-094283-B-C32, the Project RESCATA under Grant TIN2015-65100-R, the Project PROMETEO/2018/089, and the Lucentia AGI Grant.
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Kauffmann, E., Gil, D., Peral, J., Ferrández, A., Sellers, R. (2019). A Step Further in Sentiment Analysis Application in Marketing Decision-Making. In: Visvizi, A., Lytras, M. (eds) Research & Innovation Forum 2019. RIIFORUM 2019. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-30809-4_20
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