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.
Highlights
The long-term sustainability of companies depends, to a great extent, on their ability to properly meet customer needs
We conclude this section by emphasizing that our contribution to this state-of-the-art work is not focused on sentiment analysis (SA) techniques but on a combination of quantitative scores given by users, SA scores in a global review, and SA scores on individual characteristics extracted from product reviews in order to assist marketing managers and consumers in their decision-making processes
A company has a sustainable competitive advantage when it creates more economic value than a marginal firm in its industry and when other firms are unable to duplicate the benefits of this strategy [77]
Summary
The long-term sustainability of companies depends, to a great extent, on their ability to properly meet customer needs. A feature-based SA might offer a more detailed view of how consumers rate a product, which in the end will drive future behavior Using this feature-based analysis, marketing managers can obtain valuable information about different features of the product that would not be detected if the sentiment was classified only in terms of the whole review [9]. Our proposal can be applied to different marketplaces (e.g., Flipkart, Snapdeal) These product reviews were written by buyers and were used by new Sustainability 2019, 11, 4235 potential consumers as a source of electronic word-of-mouth to make decisions on their own purchases. Due to the complexity of the online reviews, we made use of SA and text data mining techniques to improve the marketing decision-making process through a specific extraction and analysis of positive, neutral, and negative characteristics of reviewed products.
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