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Monitoring user feedback is of central importance in the iterative and incremental appraoch to improvement of mobile applications. Knowledge from user review on mobile applications can be fruitful source of user feedback on firms' effort to improve their mobile applications. This paper proposes an approach to assessing the efficacy of improvements in user satisfaction for mobile applications using user review data. Specifically, overall satisfaction score and frequencies of updated features in review data are compared before and after updates of mobile applications. We believe our method can facilitate utilizing user reviews to track user feedback and obtain useful knowledge in planning and managing updates of mobile applications, and serve as a starting point of more general model.

Recognizing and understanding of human emotion is a strongly reasonable method for the interaction among human and machine communication system. The most expressive way to extract and recognize the humane emotion is through facial expression analysis. This paper presents and implements an automatic extracting, understand and recognizing method of facial expression and emotion from input still images. To evaluate the performance of the proposed scheme, we perform real implementation and experiment to verify the efficiency of emotional recognition with expressive facial database. The obtained results reveal good performance.