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We propose an automatic setting of multiple skeletal tracking Kinect cameras, in lieu of mere using a single camera, to capture a human skeleton because of possible viewing occlusions. Using multiple cameras from different angles gives a more complete whole body; however, more required steps are needed in combining multiple skeletons into one final skeleton. One camera is used as a reference for the other cameras to transform their coordinates into the reference camera's coordinate system. Once every view is in the same coordinate, one skeleton is able to be composed. Due to camera's sensory errors, nevertheless, the supposedly same joint of the skeleton, which is obtained from the transformations, may not be exactly located at the same position. Therefore, average joints are used for the composed skeleton. The skeleton is then used to analyze the walking posture of a human subject in order to check whether or not the walking is balanced.

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This study aims to identify the features of Market-Generated Content (MGC) tweets that are posted with a purpose of initiating electronic word of mouth (known as eWOM). A successful tweet is a tweet that earns active participation from the customers, e.g. getting Retweeted (RT) or Favorited (FAV). The phenomenon of eWOM effectively helps to propagate marketing agendas in both short-term marketing campaigns and long-term brand awareness. In the analysis process, logistic regression and Association rule methods were applied to mine the significant features on the MGC posts which were collected from four selected banks in Thailand during a specific period of time. For results, logistic regression indicated a set of features that causes a substantial number of RT and FAV. Additionally, the Apriori algorithm of the Association rule further specified two key features for effective RT and FAV, and it also suggested how to combine other features with those two key features to enhance the gain of RT and FAV.

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