Abstract
The active growth of Internet-based applications such as social networks and e-commerce websites leads people to generate a tremendous amount of opinions and reviews about products and services. Thus, it becomes very crucial to automatically process them. Over the last ten years, many systems have been proposed to generate and visualize reputation by mining textual and numerical reviews. However, they have neglected the fact that online reviews could be posted by malicious users that intend to affect the reputation of the target product. Besides, these systems provide an overall reputation value toward the entity and disregard generating reputation scores toward each aspect of the product. Therefore, we developed a system that incorporates spam filtering, review popularity, review posting time, and aspect-based sentiment analysis to generate accurate and reliable reputation values. The proposed model computes numerical reputation values for an entity and its aspects based on opinions collected from various platforms. Our proposed system also offers an advanced visualization tool that displays detailed information about its output. Experiment results conducted on multiple datasets collected from various platforms (Twitter, Facebook, Amazon <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\dots $ </tex-math></inline-formula> ) show the efficacy of the proposed system compared with state-of-the-art reputation generation systems.
Highlights
Having easy access to the web has radically changed the way people interact with brands and products
These systems have not taken into consideration (1) extracting and processing reviews from various platforms, (2) filtering reviews written by potential spammers, (3) generating a numerical reputation value toward each aspect of the target product, and, (4) providing an advanced reputation visualization tool for a better decision-making process
Since the local context focus (LCF)-ATEPC model currently achieves state-of-theart performance on aspect term extraction task (ATE) and Aspect polarity classification (APC) tasks2, it was selected to be employed in this paper
Summary
Having easy access to the web has radically changed the way people interact with brands and products. 1https://business.trustpilot.com/reviews many reputation generation systems have been proposed [1] [2] [3] [4] [5] [6] [7] [8] to generate and visualize reputation of online products and services based on fusing and mining textual and numerical reviews. These systems have not taken into consideration (1) extracting and processing reviews from various platforms, (2) filtering reviews written by potential spammers, (3) generating a numerical reputation value toward each aspect of the target product, and, (4) providing an advanced reputation visualization tool for a better decision-making process.
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