Mining social network users opinions' to aid buyers' shopping decisions

More and more online buyers turn to online reviews, while shopping, to get support in their choices. For instance, D'Avanzo and Kuflik (2013) show that more than 80% of buyers, while shopping online, expect user's or professional reviews services, implemented on the seller's website, that can be consulted before their purchase could take place. However, the diffusion of information, that buyers deal with during their shopping experience, makes room to the information and cognitive overload an out-and-out curse. All that is causing sellers adding Web decision support services to help buyers with their decision-making processes and there is a growing number of studies focusing on the enhancing of buyers online shopping decisions with the aim to improve their subjective attitudes towards shopping decisions. More and more sellers add on their side web decision support services that implement decision strategies employed by individuals to arrive at decisions and purchases. This paper introduces a cognitively based procedure (Gopnik et al., 2004) that mines users opinions from specific kinds of market, visually summarizing them in order to alleviate buyers overload and speeding up her/his shopping activity. The proposed approach emulates Vygotsky's theory of zone of proximal development that is well-known in the collaborative learning community (Chiu, 2000).