SME Credit Scoring Using Social Media Data

Credit analysis is required in a wide variety of decision of a modern economy.It includes understanding the credit risk of small-medium enterprises (SMEs),which today is the most significant contributor to the economy of almost everynation. Creditors usually use credit scoring as a tool to predict the probability ofthe SMEs to default in the future. The existing methods of SMEs credit scoringstill rely on traditional data, which may require high cost and have low scalability.This thesis proposed an alternative approach of credit scoring for small-mediumenterprises (SMEs), which incorporate a novel set of features extracted from socialmedia data.As a study case, we generate the credit scoring dataset which contains 20traditional features and 35 social media features to quantify the creditworthinessof more than 20,000 SMEs. The social media features are formulated basedon the previous studies in the adoption of social media data for personal creditscoring and the social media metrics for quantifying business social perception.To build the dataset, we develop the method to collect the information from somepublic websites and SMEs’ Facebook page.We conduct some experiments to develop credit scoring model for SMEs.We found that using only the social media features insufficient to model SMEsdefault in the future. However, by combining both social media features to buildthe credit scoring model, we will get better performance compared to the modeldeveloped using only traditional data

DUT-MMSR at MediaEval 2017: Predicting Media Interestingness Task

This paper describes our approach for the submission to the Media-eval 2017 Predicting Media Interestingness Task, which was particularlydeveloped for the Image subtask. An approach using a late fusion strategy is employed, combining classifiers from different features by stacking them using logistic regression (LR). As the task ground truth was based on pairwise evaluation of shots or keyframe images within the same movie, next to using precomputed features as-is, we also include a more contextual feature, considering aver-aged feature values over each movie. Furthermore, we also consider evaluation outcomes for the heuristic algorithm that yielded the highest MAPscore on the 2016 Image subtask. Considering results obtained for the development and test sets, our late fusion method shows consistent performance on the Image subtask, but not on the Video subtask. Furthermore, clear differences can be observed between MAP@10 and MAP scores.Multimedia Computin