DomainSenticNet: An Ontology and a Methodology Enabling Domain-Aware Sentic Computing

In recent years, SenticNet and OntoSenticNet have represented important developments in the novel interdisciplinary field of research known as sentic computing, enabling the development of a variety of Sentic applications. In this paper, we propose an extension of the OntoSenticNet ontology, named DomainSenticNet, and contribute an unsupervised methodology to support the development of domain-aware Sentic applications. We developed an unsupervised methodology that, for each concept in OntoSenticNet, mines semantically related concepts from WordNet and Probase knowledge bases and computes domain distributional information from the entire collection of Kickstarter domain-specific crowdfunding campaigns. Subsequently, we applied DomainSenticNet to a prototype tool for Kickstarter campaign authoring and success prediction, demonstrating an improvement in the interpretability of sentiment intensities. DomainSenticNet is an extension of the OntoSenticNet ontology that integrates each of the 100,000 concepts included in OntoSenticNet with a set of semantically related concepts and domain distributional information. The defined unsupervised methodology is highly replicable and can be easily adapted to build similar domain-aware resources from different domain corpora and external knowledge bases. Used in combination with OntoSenticNet, DomainSenticNet may favor the development of novel hybrid aspect-based sentiment analysis systems and support further research on sentic computing in domain-aware applications

Emotional Intensity-based Success Prediction Model for Crowdfunded Campaigns

We present a novel framework to predict the success of Kickstarter campaigns based on the emotional intensity induced by domain specific aspects. The framework enables to automatically mine (from campaign descriptions and product reviews) clusters of aspects characterizing a domain of interest. A Need Index-based model is built in order to predict whether a campaign will result in success (i.e., reach its funding goal). The easy to interpret Need Index representation enables to understand and monitor the most relevant domain aspects and their related emotional intensities. We tested our framework on Kickstarter campaigns in the dominant domain of mobile games with a prediction accuracy of 94.4%. The methodology opens new ground for further interdisciplinary research on causal inference to support predictions related to customer needs, particularly in the areas of behavioural economics, marketing, brand management and market research