A Three-Phase Approach for Exploiting Opinion Mining in Computational Advertising

In the past decade, advertising has rapidly become a major profit source for web-based businesses. One of the main challenges is the creation of effective and engaging ads that attract potential customers to product websites and eventual purchase. A promising solution is the combination of user preferences from the content provided on social media and the extraction of significant aspects discussed on review websites. The author proposes a three-phase model for content analysis on product review sites that considers in tandem the aspects discussed by users on review websites, the opinion polarities associated with such aspects, and user profiles on social networks, aiming to detect the most “interesting” aspects and use them to generate attractive messages. The effectiveness of the proposed approach has been validated in a real-world scenario by working with two Twitter seeds about domain-specific magazines and the data of their respective followers. Results demonstrate how the messages created by analyzing content (that is, product aspects and associated polarities) provided by a specific community improves the overall attractiveness of the generated advertisements.

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