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

Using Twitter sentiment and emotions analysis of Google Trends for decisions making

Purpose: An ever-growing body of knowledge demonstrates the correlation among real-world phenomena and search query data issued on Google, as showed in the literature survey introduced in the following. The purpose of this paper is to introduce a pipeline, implemented as a web service, which, starting with recent Google Trends, allows a decision maker to monitor Twitterâ\u80\u99s sentiment regarding these trends, enabling users to choose geographic areas for their monitors. In addition to the positive/negative sentiments about Google Trends, the pipeline offers the ability to view, on the same dashboard, the emotions that Google Trends triggers in the Twitter population. Such a set of tools, allows, as a whole, monitoring real-time on Twitter the feelings about Google Trends that would otherwise only fall into search statistics, even if useful. As a whole, the pipeline has no claim of prediction over the trends it tracks. Instead, it aims to provide a user with guidance about Google Trends, which, as the scientific literature demonstrates, is related to many real-world phenomena (e.g. epidemiology, economy, political science). Design/methodology/approach: The proposed experimental framework allows the integration of Google search query data and Twitter social data. As new trends emerge in Google searches, the pipeline interrogates Twitter to track, also geographically, the feelings and emotions of Twitter users about new trends. The core of the pipeline is represented by a sentiment analysis framework that make use of a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations to a collection of tweets geolocalized on the microblogging platform. The pipeline is accessible as a web service for any user authorized with credentials. Findings: The employment of the pipeline for three different monitoring task (i.e. consumer electronics, healthcare, and politics) shows the plausibility of the proposed approach in order to measure social media sentiments and emotions concerning the trends emerged on Google searches. Originality/value: The proposed approach aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends

Taxonomy-based detection of user emotions for advanced artificial intelligent applications

Taxonomy-Based Detection of User Emotions for Advanced Artificial Intelligent Application