11 research outputs found

    BERT, SHAP, Kano ๋ชจ๋ธ์„ ๊ธฐ๋ฐ˜ํ•œ ์†Œ๋น„์ž ๋งŒ์กฑ ์š”์†Œ ๋‹ค์ด๋‚˜๋ฏน์Šค

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ฒฝ์˜๋Œ€ํ•™ ๊ฒฝ์˜ํ•™๊ณผ, 2022.2. ์˜ค์ •์„ ๊ต์ˆ˜.์ตœ๊ทผ 10๋…„ ๊ฐ„ ์˜จ๋ผ์ธ ์‡ผํ•‘ ์‚ฐ์—…์˜ ์„ฑ์žฅ์œผ๋กœ ์˜จ๋ผ์ธ ์‡ผํ•‘๋ชฐ ํ”Œ๋žซํผ์— ์˜จ๋ผ์ธ ๋ฆฌ๋ทฐ ๋“ฑ ๋ฌดํ•œํ•œ ์†Œ๋น„์ž ๋ฐ˜์‘, ๋งŒ์กฑ๋„ ๊ด€๋ จ ์ •๋ณด๊ฐ€ ์ƒ์„ฑ๋˜๊ณ  ์žˆ๋‹ค. ์ด์— ๋งŽ์€ ๊ธฐ์—…๋“ค๊ณผ ํ•™๊ณ„์—์„œ ์ด๋ฅผ ํ™œ์šฉํ•˜์—ฌ VoC (Voice of Customer)๋ฅผ ๋ฐ˜์˜ํ•œ ์†Œ๋น„์ž ๋งŒ์กฑ๋„ ๋ชจ๋ธ๋ง์„ ์‹œ๋„ํ•˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ BERT, GBM, SHAP ๋“ฑ์„ ํ™œ์šฉํ•˜์—ฌ ์นด๋…ธ ๋ชจ๋ธ (Kano Model)์— ๊ธฐ๋ฐ˜ํ•œ ์†Œ๋น„์ž ๋งŒ์กฑ๋„ ํŠน์„ฑ (Customer Satisfaction Dimension)์„ ๋ถ„๋ฅ˜ํ•˜๊ณ  ๊ฐ ํŠน์„ฑ์˜ ์†Œ๋น„์ž ์š”๊ตฌ ์ถฉ์กฑ ์—ฌ๋ถ€๊ฐ€ ์†Œ๋น„์ž ๋งŒ์กฑ๋„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๋„๋ฅผ ์ธก์ •ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์˜ ๋ฐฉ๋ฒ•๋ก ์— ํ™œ์šฉ๋œ ๊ฐ ๋น…๋ฐ์ดํ„ฐ ๋ชจ๋ธ ์„ฑ๋Šฅ๊ณผ ์„ ํ–‰ ์—ฐ๊ตฌ๋“ค์—์„œ ์‚ฌ์šฉ๋œ ๋ชจ๋ธ ์„ฑ๋Šฅ์„ ์ง์ ‘ ๊ตฌํ˜„ ๋ฐ ๋น„๊ตํ•˜์—ฌ, ๋ณธ ๋…ผ๋ฌธ์—์„œ ํ™œ์šฉ๋œ ๋ชจ๋ธ๋“ค์˜ ์ •ํ™•์„ฑ๊ณผ ์•ˆ์ •์„ฑ์„ ๋ณด์˜€๋‹ค. ๋˜ํ•œ ํ•ด์„์  ๋จธ์‹ ๋Ÿฌ๋‹ ๊ธฐ๋ฒ•์ธ SHAP๋ฅผ ๋„์ž…ํ•˜์—ฌ, ์นด๋…ธ ์นดํ…Œ๊ณ ๋ฆฌ๋ฅผ ๋ถ„๋ฅ˜ํ•˜๋Š” ํ†ต์ผ๋œ ๋ถ„๋ฅ˜ ๋ฐฉ์‹์„ ์ œ์•ˆํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ œ์‹œ๋œ ๋ฐฉ๋ฒ•๋ก ์„ ํ†ตํ•ด ์Šค๋งˆํŠธํฐ ๋ฐ ์Šค๋งˆํŠธ์›Œ์น˜ ์ œํ’ˆ๊ตฐ์„ ๋Œ€์ƒ์œผ๋กœ ์‹ค์ฆ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜๋ฉฐ, ์‚ฐ์—…๊ณ„์— ์ œํ’ˆ ๊ฐœ๋ฐœ ๋ฐ ๊ฐœ์„ , ๊ณ ๊ฐ ์„ธ๋ถ„ํ™” ์ „๋žต ๋“ฑ ๊ธฐ์—… ์˜์‚ฌ๊ฒฐ์ • ๋ฐฉํ–ฅ์„ฑ์— ์œ ์˜๋ฏธํ•œ ์ œ์–ธ์„ ์ œ์‹œํ•จ์œผ๋กœ์จ ๋ณธ ๋ฐฉ๋ฒ•๋ก ์˜ ์‹ค์šฉ์  ๊ฐ€์น˜๋ฅผ ์ž…์ฆํ•˜์˜€๋‹ค.As a large number of online reviews are loaded on e-commerce platforms in recent days, companies are being able to measure customer satisfaction reflecting VoC (Voice of Customer) with big data analytics. This paper proposes the improved framework for identifying characteristics of customer satisfaction dimensions (CSD) based on Kano model using BERT (Bidirectional Encoder Representations from Transformers), GBM (Gradient Boosting Machine), and SHAP (Shapley Additive eXplanation). We proved each model outperformance by comparing other models which previous studies have used. And this paper suggests the unified rule of Kano model classification using SHAP. Furthermore, we conducted empirical studies regarding smartphone and smartwatch products which suggest the direction of product enhancement/development strategy and multi-product level customer segmentation strategy to product manufacturers. This shows proposed methodologyโ€™s effectiveness and usefulness on industrial analysis.1. Introduction 1 2. A framework for modelling customer satisfaction from online review 5 3. Research Method 8 3.1 Mining customerโ€™s sentiments toward CSDs from online reviews 8 3.2 Measuring the effects of customer sentiments toward each CSD on customer satisfaction 11 3.3 Identifying the feature of each CSD from the customerโ€™s perspective 11 3.4 Classifying each CSD into Kano categories 14 4. Empirical Study 17 4.1 Study 1 17 4.2 Study 2 24 5. Conclusion 27 6. Reference 29์„

    Mixture extreme learning machine algorithm for robust regression

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    The extreme learning machine (ELM) is a well-known approach for training single hidden layer feedforward neural networks (SLFNs) in machine learning. However, ELM is most effective when used for regression on datasets with simple Gaussian distributed error because it often employs a squared loss in its objective function. In contrast, real-world data is often collected from unpredictable and diverse contexts, which may contain complex noise that cannot be characterized by a single distribution. To address this challenge, we propose a robust mixture ELM algorithm, called Mixture-ELM, that enhances modeling capability and resilience to both Gaussian and non-Gaussian noise. The Mixture-ELM algorithm uses an adjusted objective function that blends Gaussian and Laplacian distributions to approximate any continuous distribution and match the noise. The Gaussian mixture accurately models the residual distribution, while the inclusion of the Laplacian distribution addresses the limitations of the Gaussian distribution in identifying outliers. We derive a solution to the novel objective function using the expectation maximization (EM) and iteratively reweighted least squares (IRLS) algorithms. We evaluate the effectiveness of the algorithm through numerical simulation and experiments on benchmark datasets, thereby demonstrating its superiority over other state-of-the-art machine learning methods in terms of robustness and generalization

    Robust Extreme Learning Machine for Modeling with Unknown Noise

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    Extreme learning machine (ELM) is an emerging machine learning technique for training single hidden layer feedforward networks (SLFNs). During the training phase, ELM model can be created by simultaneously minimizing the modeling errors and norm of the output weights. Usually, squared loss is widely utilized in the objective function of ELMs, which is theoretically optimal for the Gaussian error distribution. However, in practice, data collected from uncertain and heterogeneous environments trivially result in unknown noise, which may be very complex and cannot be described well using any single distribution. In order to tackle this issue, in this paper, a robust ELM (R-ELM) is proposed for improving the modeling capability and robustness with Gaussian and non-Gaussian noise. In R-ELM, a modified objective function is constructed to fit the noise using mixture of Gaussian (MoG) to approximate any continuous distribution. In addition, the corresponding solution for the new objective function is developed based on expectation maximization (EM) algorithm. Comprehensive experiments, both on selected benchmark datasets and real world applications, demonstrate that the proposed R-ELM has better robustness and generalization performance than state-of-the-art machine learning approaches

    Semi-supervised learning for big social data analysis

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    In an era of social media and connectivity, web users are becoming increasingly enthusiastic about interacting, sharing, and working together through online collaborative media. More recently, this collective intelligence has spread to many different areas, with a growing impact on everyday life, such as in education, health, commerce and tourism, leading to an exponential growth in the size of the social Web. However, the distillation of knowledge from such unstructured Big data is, an extremely challenging task. Consequently, the semantic and multimodal contents of the Web in this present day are, whilst being well suited for human use, still barely accessible to machines. In this work, we explore the potential of a novel semi-supervised learning model based on the combined use of random projection scaling as part of a vector space model, and support vector machines to perform reasoning on a knowledge base. The latter is developed by merging a graph representation of commonsense with a linguistic resource for the lexical representation of affect. Comparative simulation results show a significant improvement in tasks such as emotion recognition and polarity detection, and pave the way for development of future semi-supervised learning approaches to big social data analytics

    Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines

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    In the past decade, deep learning techniques have powered many aspects of our daily life, and drawn ever-increasing research interests. However, conventional deep learning approaches, such as deep belief network (DBN), restricted Boltzmann machine (RBM), and convolutional neural network (CNN), suffer from time-consuming training process due to fine-tuning of a large number of parameters and the complicated hierarchical structure. Furthermore, the above complication makes it difficult to theoretically analyze and prove the universal approximation of those conventional deep learning approaches. In order to tackle the issues, multilayer extreme learning machines (ML-ELM) were proposed, which accelerate the development of deep learning. Compared with conventional deep learning, ML-ELMs are non-iterative and fast due to the random feature mapping mechanism. In this paper, we perform a thorough review on the development of ML-ELMs, including stacked ELM autoencoder (ELM-AE), residual ELM, and local receptive field based ELM (ELM-LRF), as well as address their applications. In addition, we also discuss the connection between random neural networks and conventional deep learning

    A review of sentiment analysis research in Arabic language

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    Sentiment analysis is a task of natural language processing which has recently attracted increasing attention. However, sentiment analysis research has mainly been carried out for the English language. Although Arabic is ramping up as one of the most used languages on the Internet, only a few studies have focused on Arabic sentiment analysis so far. In this paper, we carry out an in-depth qualitative study of the most important research works in this context by presenting limits and strengths of existing approaches. In particular, we survey both approaches that leverage machine translation or transfer learning to adapt English resources to Arabic and approaches that stem directly from the Arabic language

    Bayesian network based extreme learning machine for subjectivity detection

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    Subjectivity detection is a task of natural language processing that aims to remove โ€˜factualโ€™ or โ€˜neutralโ€™ content, i.e., objective text that does not contain any opinion, from online product reviews. Such a pre-processing step is crucial to increase the accuracy of sentiment analysis systems, as these are usually optimized for the binary classification task of distinguishing between positive and negative content. In this paper, we extend the extreme learning machine (ELM) paradigm to a novel framework that exploits the features of both Bayesian networks and fuzzy recurrent neural networks to perform subjectivity detection. In particular, Bayesian networks are used to build a network of connections among the hidden neurons of the conventional ELM configuration in order to capture dependencies in high-dimensional data. Next, a fuzzy recurrent neural network inherits the overall structure generated by the Bayesian networks to model temporal features in the predictor. Experimental results confirmed the ability of the proposed framework to deal with standard subjectivity detection problems and also proved its capacity to address portability across languages in translation tasks
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