Extraction and classification of facemarks

We propose methods for extracting facemarks (emoticons) in text and classifying them into some emotional categories. In text-based communication, facemarks have gained popularity, since they help us understand what writers imply. However, there are two problems in text-based communication using facemarks; the first is the variety of facemarks and the second is lack of good comprehension in using facemarks. These problems are more serious in the areas where 2-byte characters are used, because the 2-byte characters can generate a quite large number of different facemarks. Therefore, we are going to propose methods for extraction and classification of facemarks. Regarding the extraction of facemarks as a chunking task, we automatically annotate a tag to each character in text. In the classification of the extracted facemarks, we apply the dynamic time alignment kernel (DTAK) and the string subsequence kernel (SSK) for scoring in the k-nearest neighbor (k-NN) method and for expanding usual Support Vector Machines (SVMs) to accept sequential data such as facemarks. We empirically show that our methods work well in classification and extraction of facemarks, with appropriate settings of parameters.