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Open AccessArticle

Managing Marketing Decision-Making with Sentiment Analysis: An Evaluation of the Main Product Features Using Text Data Mining

1
School of Industrial Engineering, University of Costa Rica, San José 11501-2060, Costa Rica
2
Department of Software and Computing Systems, University of Alicante, 03690 Alicante, Spain
3
Department of Computer Technology and Computation, University of Alicante, 03690 Alicante, Spain
4
Department of Marketing, University of Alicante, 03690 Alicante, Spain
*
Authors to whom correspondence should be addressed.
Sustainability 2019, 11(15), 4235; https://doi.org/10.3390/su11154235
Received: 6 June 2019 / Revised: 29 July 2019 / Accepted: 29 July 2019 / Published: 5 August 2019
Companies have realized the importance of “big data” in creating a sustainable competitive advantage, and user-generated content (UGC) represents one of big data’s most important sources. From blogs to social media and online reviews, consumers generate a huge amount of brand-related information that has a decisive potential business value for marketing purposes. Particularly, we focus on online reviews that could have an influence on brand image and positioning. Within this context, and using the usual quantitative star score ratings, a recent stream of research has employed sentiment analysis (SA) tools to examine the textual content of reviews and categorize buyer opinions. Although many SA tools split comments into negative or positive, a review can contain phrases with different polarities because the user can have different sentiments about each feature of the product. Finding the polarity of each feature can be interesting for product managers and brand management. In this paper, we present a general framework that uses natural language processing (NLP) techniques, including sentiment analysis, text data mining, and clustering techniques, to obtain new scores based on consumer sentiments for different product features. The main contribution of our proposal is the combination of price and the aforementioned scores to define a new global score for the product, which allows us to obtain a ranking according to product features. Furthermore, the products can be classified according to their positive, neutral, or negative features (visualized on dashboards), helping consumers with their sustainable purchasing behavior. We proved the validity of our approach in a case study using big data extracted from Amazon online reviews (specifically cell phones), obtaining satisfactory and promising results. After the experimentation, we could conclude that our work is able to improve recommender systems by using positive, neutral, and negative customer opinions and by classifying customers based on their comments. View Full-Text
Keywords: big data; sentiment analysis; marketing decisions; feature selection big data; sentiment analysis; marketing decisions; feature selection
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MDPI and ACS Style

Kauffmann, E.; Peral, J.; Gil, D.; Ferrández, A.; Sellers, R.; Mora, H. Managing Marketing Decision-Making with Sentiment Analysis: An Evaluation of the Main Product Features Using Text Data Mining. Sustainability 2019, 11, 4235. https://doi.org/10.3390/su11154235

AMA Style

Kauffmann E, Peral J, Gil D, Ferrández A, Sellers R, Mora H. Managing Marketing Decision-Making with Sentiment Analysis: An Evaluation of the Main Product Features Using Text Data Mining. Sustainability. 2019; 11(15):4235. https://doi.org/10.3390/su11154235

Chicago/Turabian Style

Kauffmann, Erick; Peral, Jesús; Gil, David; Ferrández, Antonio; Sellers, Ricardo; Mora, Higinio. 2019. "Managing Marketing Decision-Making with Sentiment Analysis: An Evaluation of the Main Product Features Using Text Data Mining" Sustainability 11, no. 15: 4235. https://doi.org/10.3390/su11154235

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