• Information Systems Review
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• v.4 no.1
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• pp.131-143
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• 2002

Numerous types of SIS (Strategic Information Systems) have been developed based on certain strategic frameworks. This paper reviews those traditional SIS frameworks, and points out the ignorance of customer-orientation. Also, this paper addresses a new SIS framework based on customer-orientation: i.e., transaction visibility. This paper proposes that computer systems can increase customer value by changing visibility in transactions with customers. Relevant cases are also presented for sake of clear understanding of this new framework.

• International Journal of Automotive Technology
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• v.1 no.1
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• pp.1-8
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• 2000

This paper reviews the main drivers forcing change and progress in powertrains for passenger cars in the coming years. The environmental drivers of omissions and CO2 will force better technical performance, but customer demand for increased choice will force change in the basic engine design and provide opportunities for alternate configurations of powertrain. Gasoline engines will embody refinements of valve train actuations as well as developments in combustion, especially direct injection and possibly a lean booated form of direct injection. Nevertheless, the conventional, port injected engine will continue to be the dominant engine for some years to come. The high speed direct injection diesel will very soon supplant its indirect injection predecessor completely. It will take an increasing share of the total powertrain market as improved specific power and refinement make it even more attractive to the customer. Car manufacturers will provide diesel models to satisfy this customer demand as well as using the efficiency of the diesel to enable them to meet their fleet CO2 commitments. Both gasoline and diesel engines will see an increasing degree of electrification and partial hybridisation as efficient flywheel mounted electrical devices become available.

• ETRI Journal
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• v.33 no.5
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• pp.720-730
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• 2011

Opinion mining involves the analysis of customer opinions using product reviews and provides meaningful information including the polarity of the opinions. In opinion mining, feature extraction is important since the customers do not normally express their product opinions holistically but separately according to its individual features. However, previous research on feature-based opinion mining has not had good results due to drawbacks, such as selecting a feature considering only syntactical grammar information or treating features with similar meanings as different. To solve these problems, this paper proposes an enhanced feature extraction and refinement method called FEROM that effectively extracts correct features from review data by exploiting both grammatical properties and semantic characteristics of feature words and refines the features by recognizing and merging similar ones. A series of experiments performed on actual online review data demonstrated that FEROM is highly effective at extracting and refining features for analyzing customer review data and eventually contributes to accurate and functional opinion mining.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.586-598
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• 2021

Nowadays, users' reviews and feedback on e-commerce sites stored in text create a huge source of information for analyzing customers' experience with goods and services provided by a business. In other words, collecting and analyzing this information is necessary to better understand customer needs. In this study, we first collected a corpus with 99,322 customers' comments and opinions in English. From this corpus we chose the best number of topics (K) using Perplexity and Coherence Score measurements as the input parameters for the model. Finally, we conducted an experiment using the latent Dirichlet allocation (LDA) topic model with K coefficients to explore the topic. The model results found hidden topics and keyword sets with high probability that are interesting to users. The application of empirical results from the model will support decision-making to help businesses improve products and services as well as business management and development in the field of hotel services.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.325-345
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• 2023

In the rapidly evolving domain of e-commerce, our study presents a cohesive approach to enhance customer satisfaction prediction from online reviews, aligning methodological innovation with practical insights. We integrate the RFE-SHAP feature selection with LDA topic modeling to streamline predictive analytics in e-commerce. This integration facilitates the identification of key features-specifically, narrowing down from an initial set of 28 to an optimal subset of 14 features for the Random Forest algorithm. Our approach strategically mitigates the common issue of overfitting in models with an excess of features, leading to an improved accuracy rate of 84% in our Random Forest model. Central to our analysis is the understanding that certain aspects in review content, such as quality, fit, and durability, play a pivotal role in influencing customer satisfaction, especially in the clothing sector. We delve into explaining how each of these selected features impacts customer satisfaction, providing a comprehensive view of the elements most appreciated by customers. Our research makes significant contributions in two key areas. First, it enhances predictive modeling within the realm of e-commerce analytics by introducing a streamlined, feature-centric approach. This refinement in methodology not only bolsters the accuracy of customer satisfaction predictions but also sets a new standard for handling feature selection in predictive models. Second, the study provides actionable insights for e-commerce platforms, especially those in the clothing sector. By highlighting which aspects of customer reviews-like quality, fit, and durability-most influence satisfaction, we offer a strategic direction for businesses to tailor their products and services.

• Information Systems Review
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• v.17 no.3
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• pp.95-111
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• 2015

The recommendation system automatically provides the predicted items which are expected to be purchased by analyzing the previous customer behaviors. This recommendation system has been applied to many e-commerce businesses, and it is generating positive effects on user convenience as well as the company's revenue. However, there are several limitations of the existing recommendation systems. They do not reflect specific criteria for evaluating products or the factors that affect customer buying decisions. Thus, our research proposes a collaborative recommendation model algorithm that utilizes each customer's online product reviews. This study deploys topic modeling method for customer opinion mining. Also, it adopts a kernel-based machine learning concept by selecting kernels explaining individual similarities in accordance with customers' purchase history and online reviews. Our study further applies a multiple kernel learning algorithm to integrate the kernelsinto a combined model for predicting the product ratings, and it verifies its validity with a data set (including purchased item, product rating, and online review) of BestBuy, an online consumer electronics store. This study theoretically implicates by suggesting a new method for the online recommendation system, i.e., a collaborative recommendation method using topic modeling and kernel-based learning.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.595-607
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• 2021

Purpose: This study introduces a systematic framework to evaluate service satisfaction of cosmetic brands through online review analysis utilizing Text-Mining technique. Methods: The framework assumes that the service satisfaction is evaluated by positive comments from online reviews. That is, the service satisfaction of a cosmetic brand is evaluated higher as more positive opinions are commented in the online reviews. This study focuses on two approaches. First, it collects online review comments from the top 50 global cosmetic brands and evaluates customer service satisfaction for each cosmetic brands by applying Sentimental Analysis and Latent Dirichlet Allocation. Second, it analyzes the determinants that induce or influence service satisfaction and suggests the guidelines for cosmetic brands with low satisfaction to improve their service satisfaction. Results: For the satisfaction evaluation, online review data were extracted from the top 50 global cosmetic brands in the world based on 2018 sales announced by Brand Finance in the UK. As a result of the satisfaction analysis, it was found that overall there were more positive opinions than negative opinions and the averages for polarity, subjectivity, positive ratio, and negative ratio were calculated as 0.50, 0.76, 0.57, and 0.19, respectively. Polarity, subjectivity and positive ratio showed the opposite pattern to negative ratio, and although there was a slight difference in fluctuation range and ranking between them, the patterns are almost same. Conclusion: The usefulness of the proposed framework was verified through case study. Although some studies have suggested a method to analyze online reviews, they didn't deal with the satisfaction evaluation among competitors and cause analysis. This study is different from previous studies in that it evaluates service satisfaction from a relative point of view among cosmetic brands and analyze determinants.

• The Journal of Bigdata
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• v.9 no.1
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• pp.49-60
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• 2024

This study aims to provide insights for developing innovative products, based on reviews from females aged 30 to 70 who bought cosmetic cushions via TV home shopping. Analyzing 200,000 reviews with Selenium and NLP techniques, we found the main audience is in their 50s and 60s, prioritizing radiance, blemish and wrinkle coverage, and adherence. Notably, products with appealing designs were preferred, especially for gifting among relatives and friends. The proposed innovation is Korea's first AI-recommended cushion, utilizing NLP to match customer needs. Key ingredient recommendations include S.Acamella extract and AHA components, chosen for their perceived benefits and consumer preference. The research also highlights the importance of product aesthetics and gift potential, suggesting marketing strategies should emphasize these aspects to appeal to the target demographic. This approach aims to guide product development and marketing towards meeting consumer expectations in the cosmetic cushion industry, making products more personalized and gift-worthy.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.11-19
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• 2024

Online and mobile reviews strongly influence consumer behavior, especially in the service industry, and play a key role in determining customer retention and revisit rates. Systematically analyzing the information in these reviews can effectively assess how they directly influence customers' purchase decisions. In this study, we applied the existing KNU sentiment dictionary to food and beverage (F&B) review data to build a customized sentiment lexicon using N-grams based on about 10,000 reviews. Comparing its performance with the existing dictionary, we found that the sentiment lexicon generated using the 1-gram, 2-gram, and 3-gram models had the highest accuracy, precision, recall, and F1 scores. These results can serve as a powerful business support tool for SMEs in the F&B and grocery shopping sector, also be used to predict customer demand for technology and policy.

• The Journal of the Korea Contents Association
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• v.13 no.10
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• pp.533-546
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• 2013

This study analyzed the contents of online hotel reviews of Benikea hotels. The results were as follows: First, the outstanding customer value were functional value, emotional value, price/value for money and epistemic value, conditional value are next. Social value was not found. Functional value was provoked by the functions of hotel room, room amenities, room view, room cleaness, restaurant service, and hotel staff friendliness as human services. Emotional value was the emotional response to the qualities of hotel's functions. Price/value for money was a perceived value of hotel user by the comparison of what to invest with what to receive. From the results, it can be proposed that hotel should maintain the basic qualities of core functions of hotel.