Recently, the importance of mobile cloud computing has increased. Mobile devices can collect personal data from various sensors within a shorter period of time and sensor-based data consists of valuable information from users. Advanced computation power and data analysis technology based on cloud computing provide an opportunity to classify massive sensor data into given labels. Random forest algorithm is known as black box model which is hardly able to interpret the hidden process inside. In this paper, we propose a method that analyzes the variable impact in random forest algorithm to clarify which variable affects classification accuracy the most. We apply Shapley Value with random forest to analyze the variable impact. Under the assumption that every variable cooperates as players in the cooperative game situation, Shapley Value fairly distributes the payoff of variables. Our proposed method calculates the relative contributions of the variables within its classification process. In this paper, we analyze the influence of variables and list the priority of variables that affect classification accuracy result. Our proposed method proves its suitability for data interpretation in black box model like a random forest so that the algorithm is applicable in mobile cloud computing environment.

In the airline industry, customer satisfaction occurs when passengers’ expectations are met through the airline experience. Considering that airline service quality is the main factor in obtaining new and retaining existing customers, airline companies are applying various approaches to improve the quality of the physical and social servicescapes. It is common to use data analysis techniques for analyzing customer propensity in marketing. However, their application to the airline industry has traditionally focused solely on surveys; hence, there is a lack of attention paid to deep learning techniques based on survey results. This study has two purposes. The first purpose is to find the relationship between various factors influencing customer churn risk and satisfaction by analyzing the airline customer data. For this, we applied deep learning techniques to the survey data collected from the users who have used mostly Korean airplanes. To the best of our knowledge, this is the one of the few attempts at applying deep learning to analyze airline customer propensities. The second purpose is to analyze the influence of the social servicescape, including the viewpoints of the cabin crew and passengers using aircraft, on airline customer propensities. The experimental results demonstrated that the proposed method of considering human services increased the accuracy of predictive models by up to 10% and 9% in predicting customer churn risk and satisfaction, respectively.

Twitter is one of the most popular microblogging services that lets users post short text called Tweet. Tweet is distinguished from conventional text data in that it is typically composed of short and informal message, and it makes typical text analysis methods do not work well. Accordingly, extracting meaningful topics from tweets brings up new challenges. In this work, we propose a simple and novel method called Core-Topic-based Clustering (CTC), which extracts topics and cluster tweets simultaneously based on the clustering principles: minimizing the inter-cluster similarity and maximizing the intra-cluster similarity. Experimental results show that our method efficiently extracts meaningful topics, and the clustering performance is better than K-means algorithm.

Deep learning algorithms are used in various applications for pattern recognition, natural language processing, speech recognition, and so on. Recently, neural network based natural language processing techniques use fixed length word embedding. Word embedding is a method of digitizing a word at a specific position into a low dimensional dense vector with fixed length while preserving the similarity of the distribution of its surrounding words. Currently, the word embedding methods for foreign language are used for Korean words, however, existing word embedding methods are developed for English originally, so they do not reflect the order and structure of the Korean words. In this paper, we propose a word embedding method for Korean, which is called Skip-gram-KR, and a Korean affix tokenizer. Skip-gram-KR creates similar word training data through backward mapping and two-word skipping method. Experiment results show the proposed method achieved the most accurate performance.