SCI-F: Social-Corporate Data Integration Framework Abla Lotfy Information Systems Department Faculty of Computers and Information Cairo University Giza, Egypt +20235674630 abla@fci-cu.edu.eg Neamat El Tazi Information Systems Department Faculty of Computers and Information Cairo University Giza, Egypt +20235674630 n.eltazi@fci-cu.edu.eg Neveen ElGamal Information Systems Department Faculty of Computers and Information Cairo University Giza, Egypt +20235674630 n.elgamal@fci-cu.edu.eg ABSTRACT Opinions are considered a powerful source of market research. Social networks are the most popular mean for people to state their opinions about products and services. Companies invest in analysing their customers’ opinions from multiple existing social media platforms. The knowledge extracted from social media contains sentiment data that is not included in corporate database. This extracted data can be used to improve marketing campaigns to better retain customers and meet their needs. The integration between both social media data and corporate data can lead to better insights that would not have been possible to gain without such integration. This paper proposes a framework to integrate sentiment data, extracted from customers’ opinions with corporate data of an organization. The process of integrating opinions streams into corporate database is proposed, then a multidimensional data warehouse is built over the integrated database to perform advanced analytical tasks and answer queries that would not have been possible without the integration. The new framework has been experimented with a case study of integrating opinions about digital devices and corporate data to illustrate the usage of the integrated model. Keywords Data Integration; Social Media; Sentiment Analysis; Business Intelligence; Social Business Intelligence. 1. INTRODUCTION Recently, it has become a necessity for most companies to monitor social media reviews about their products. In order to do so, companies started to manually reviewing mentions of their brands on different social media platform. The manual approach proved to be not scalable with the huge amount of reviews and does not enable companies to detect real-time customers’ insights not to engage with social customers in a relevant and timely manner. Consequently, the need for automating the process of social media monitoring arose. Moreover, in order for companies to gain insights from both social media and their corporate data, data practices need to be adapted and extended. This is to allow the seamless integration of both types of data. Opinion mining and sentiment analysis [4] is used to automatically extract knowledge/ opinion from text represented by reviews/comments and/or statuses about a particular product or topic. Social media monitoring is performed in several ways like doing sentiment analysis processes like polarity definition and feature extraction from customers’ opinions about a specific product, service or whole organization only or go through creating a database that includes historical details about analysed opinions along with their sentiment analysis results. This database can be easily accessed by business intelligence tools for querying and visualization. There is a new direction to integrate the extracted data from social media with corporate data to generate new queries and analytical tasks [1]. To go through this new direction, there is a need first to choose a suitable way to extract opinions from social media platforms on which an organization trust and then integrate the social media unstructured data and corporate data under one data structure. The integration gives the ability to perform new kind of analysis and measuring more key performance indicators that could not have been answered using only corporate data like comparing between countries to identify best country, the one that has the maximum number of positive opinions with product’s sales value for the same country with respect to organization’s sales goal. The challenge is to find a suitable way to integrate both types of data together to analyse the integrated data and get more insights. The rest of the paper is organized as follows. Section 2 presents Social Business Intelligence background. Section 3 summarizes the related work. A discussion about the proposed framework with introducing a case study leveraging that framework is presented in section 4. An evaluation of the proposed framework presented in section 5 and we conclude in section 6. 2. BACKGROUND Traditionally, business intelligence is about building reports, dashboards, scorecards and ad hoc queries which has the ability to track the business performance and various key performance indicators and is solely focused on delivering intelligence from a data warehouse and other database platforms which host the structured data. [12]. Recently, Social Business Intelligence incorporates social media monitoring and analytics with traditional business intelligence. It shifts the focus from transactional data to behavioural analytics models that improve understanding customers and enable ways to improve customer Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. 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