Document representation based on probabilistic word clustering in customer-voice classification

Pattern Analysis and Applications - Customer-voice data have an important role in different fields including marketing, product planning, and quality assurance. However, owing to the manual...     

Smartphone user segmentation based on app usage sequence with neural networks

The term user segmentation refers to classifying users into groups depending on their specific needs, characteristics, or behaviors. It is a key element of product development and marketing in many industries, such as the smartphone industry, which employs user segmentation to gather information about usage logs, to produce new products for such specific groups of users. However, previous studies on smartphone user segmentation have been primarily based on demographics and reported usage, which are inherently subjective and prone to skew by the observers and participants. Hamka et al. (2014) was the first to conduct a study, in which smartphone user segmentation was performed using log data collected through smartphone measurements. However, they focused only on network usage and the number of apps used, and not on characteristics or preferences. In this study, we proposed novel ways of segmenting smartphone users based on app usage sequences collected from smartphone logs. We proposed a variant of seq2seq architecture combining the advantages of previous deep neural networks: neural embedding architecture and seq2seq architecture. Furthermore, we compared the user segmentation results of the proposed method with an answer set of segmentation results conducted by domain experts. These experiments demonstrated that the proposed method effectively determines similarities between usage sequences and outperforms existing user segmentation methods.     

Extraction of Product Evaluation Factors with a Convolutional Neural Network and Transfer Learning

Earlier studies have indicated that decision-making by a project development team can be improved throughout the design and development process by understanding the key factors that affect customers evaluations of a new product. Aspect extraction could thus be a useful tool for identifying important attributes when evaluating products or services. Aspect extraction based on deep convolutional neural networks has recently been suggested, demonstrating state-of-the-art performance when applied to a customer review of electronic devices. However, this approach is unsuited to the rapidly evolving smartphone industry, which involves a wide range of product lines. Whereas the previous approach required significant amounts of data labeling for each product, we propose a variant of that approach that includes transfer learning. We also propose a novel approach for transferring the architecture sequentially within the product group. The results indicate that the principal key feature of each product is extracted effectively by the proposed method without having to re-train the entire convolutional neural network model. Furthermore, the proposed method performs better than the previous method for each product line.