Factors Influencing the Perception of Seller Credibility in Online Reputation System: an Eye-Movement Approach

The current online reputation systems for online sellers face great challenges from bad-faith behavior such as malicious negative reviews, click farming, mismatch between images and commodities, and forged commodities. To optimize the design of online reputation systems, explore the consumer utilization of credit clues, and describe the law of mutual trust, this paper puts forth three hypotheses about the influencing factors of consumer perception of online seller credibility and integrates various research methods such as an eye-movement experiment, questionnaire survey, econometric analysis, and empirical research. To evaluate the three hypotheses, the display modes of commodities on a current e-commerce platform were optimized, and eye-movement experiments were conducted on original and optimized webpages. Results show that the display of sales growth, the refinement and tagging of review content significantly impacted consumer perception of seller credibility. Further, designers of online reputation systems were advised to display sales trends, provide personalized sales queries, and tag a variety of reviews for consumers to easily ascertain credible sellers. This advice helped curb bad-faith behavior

MTVRep: A movie and TV show reputation system based on fine-grained sentiment and semantic analysis

Customer reviews are a valuable source of information from which we can extract very useful data about different online shopping experiences. For trendy items (products, movies, TV shows, hotels, services . . . ), the number of available users and customers’ opinions could easily surpass thousands. Therefore, online reputation systems could aid potential customers in making the right decision (buying, renting, booking . . . ) by automatically mining textual reviews and their ratings. This paper presents MTVRep, a movie and TV show reputation system that incorporates fine-grained opinion mining and semantic analysis to generate and visualize reputation toward movies and TV shows. Differently from previous studies on reputation generation that treat the task of sentiment analysis as a binary classification problem (positive, negative), the proposed system identifies the sentiment strength during the phase of sentiment classification by using fine-grained sentiment analysis to separate movie and TV show reviews into five discrete classes: strongly negative, weakly negative, neutral, weakly positive and strongly positive. Besides, it employs embeddings from language models (ELMo) representations to extract semantic relations between reviews. The contribution of this paper is threefold. First, movie and TV show reviews are separated into five groups based on their sentiment orientation. Second, a custom score is computed for each opinion group. Finally, a numerical reputation value is produced toward the target movie or TV show. The efficacy of the proposed system is illustrated by conducting several experiments on a real-world movie and TV show dataset