Distinguishing factor
We have focused on innovation as a differentiating factor in the market, contributing with differentiating solutions to the success of our projects.

Learn more about one of our projects developed in this area: System for the Semantic Analysis of Contents on Social Networks.

Project description

The main objective of the SASC-RS project is o develop a system, supported on Microsoft Dynamics CRM, that is capable of analysing impressions regarding a given entity or event, based on data gathered on social networks, with the ability to match different customer profiles and accounts. The SASC-RS will be developed aiming to resolve this problem in very pragmatic contexts, enhancing its acceptance by companies and organisations that will see it as an asset with concrete applicability from a usability standpoint. Based on this assumption, the system will be designed and created on a customer relationship management (CRM) platform.

This solution aims to provide companies with a system as a structured repository for gathering, storing and processing information aimed at its customers or the users of the service.

This project aims to create a solution that answers the urgent and growing trend for companies to adopt a clear strategy of commitment to its customers on social networks. Companies are increasingly aware that they have to think more globally and be prepared to provide information through multiple channels and different models. When a customer has a good experience with a company, it contributes to retention and loyalty and, consequently, to the improved institutional image and business results. This is how Bizdirect sees its role as contributing to the support of its current and future customers through a technologically innovative solution that is aimed at meeting this market trend.


Financing programme

This project was approved within the scope of the IS IR&D – individual project, under the terms of AAC no. 07/SI/2012



The project is co-financed by the ERDF through the Operational Programme 'Thematic Factors of Competitiveness', with a refundable incentive of 193,766.82€ corresponding to an eligible investment of 341,874.39€.



The project has a duration of 16 months, scheduled to start in March 2014 and conclude in June 2015.


Project Conclusions

The SASC-RS project was designed to develop a system capable of analysing impressions about a given entity or event, based on data gathered from social networks, with the additional advantage of using a CRM (Customer Relationship Management) solution as a repository of all the information gathered from customers, potential customers, fans or followers of a brand, for example.

Research was done and the resulting technical developments made it possible to implement a system that automatically classifies the polarity of comments gathered from social networks. This information is brought together and made available on the CRM platform, making it possible to obtain an integrated vision of the reaction of a universe of entities to a certain event or brand, for example. Other results from this work included the introduction of additional features, such as the counting of mentions of entities, such as the names of persons, brands or companies; the definition of the polarity of the impressions in the document for each identified entity; the gathering of verbs and adjectives, in a standardised form, listing their frequency of use.

The development of the communication mechanisms of the SASC-RS with the API of social networks resulted in the creation of two applications that performed the integration between the SASC-RS and the APIs of Facebook and Twitter, respectively.

The project also included the analysis needed for the mechanisms to implement identity management and to unsubscribe, defined from the start as one of the key areas of the project in terms of concerns about the security and privacy of data.

From a communication standpoint, the project and its results were and are being promoted by the Bizdirect Business Development department at events and with its installed customer base. A scientific paper was also written, and later accepted by the Society for Science and Education - United Kingdom for publication in the respected journal Transactions on Machine Learning and Artificial Intelligence.



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