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

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Title: Managing Marketing Decision-Making with Sentiment Analysis: An Evaluation of the Main Product Features Using Text Data Mining
Authors: Kauffmann, Erick | Peral Cortés, Jesús | Gil, David | Ferrández, Antonio | Sellers Rubio, Ricardo | Mora, Higinio
Research Group/s: Lucentia | Procesamiento del Lenguaje y Sistemas de Información (GPLSI) | Marketing | Informática Industrial y Redes de Computadores
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos | Universidad de Alicante. Departamento de Tecnología Informática y Computación | Universidad de Alicante. Departamento de Marketing
Keywords: Big data | Sentiment analysis | Marketing decisions | Feature selection
Knowledge Area: Lenguajes y Sistemas Informáticos | Arquitectura y Tecnología de Computadores | Comercialización e Investigación de Mercados
Issue Date: 5-Aug-2019
Publisher: MDPI
Citation: 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. doi:10.3390/su11154235
Abstract: 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.
Sponsor: This study was partially funded by the ECLIPSE-UA (RTI2018-094283-B-C32) and RESCATA (TIN2015-65100-R) projects of the Spanish Ministry of Economy and Competitiveness (MINECO) and PROMETEO/2018/089. This work was partially funded by the Spanish Ministry of Economy and Competitiveness (MINECO) under the grant Project CloudDriver4Industry TIN2017-89266-R and by the Conselleria de Educación, Investigación, Cultura y Deporte of the Community of Valencia, Spain, within the program of support for research under project AICO/2017/134.
ISSN: 2071-1050
DOI: 10.3390/su11154235
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Peer Review: si
Publisher version:
Appears in Collections:INV - LUCENTIA - Artículos de Revistas
INV - GPLSI - Artículos de Revistas
INV - MKT - Artículos de Revistas
INV - I2RC - Artículos de Revistas

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