Visualization of online customer reviews and evaluations based on Self-organizing Map

Saitoh, Fumiaki

doi:10.1109/smc.2014.6973903

Cited by 5 publications

(3 citation statements)

References 12 publications

(7 reference statements)

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“…The secondary classification used in the survey is shown in Table 1. Aigner [25] Broeksema et al [71] Bai et al [49] Lafon et al [26] Ferreira et al [54] Basole et al [58] Hao et al [42] 2014 Nicholas et al [50] Basole [76] Deligiannidis and Noyes [59] Basole and Bellamy [77] Lu et al [35] Shi et al [36] Rodden [84] Yaeli et al [21] Saitoh [43] 2015 Keahey [29] Basole et al [60] Dou et al [32] Kameoka et al [66] Nair et al [85] 2016 Roberts et al [51] Liu et al [81] Iyer and Basole [61] Basole et al [31] Wu et al [67] Nagaoka et al [22] Sijtsma et al [37] 2017 Ghooshchi et al [72] Kumar and Belwal [52] Bachhofner et al [73] Ramesh et al [34] Schotter et al [62] Kang et al [69] Fayoumi et al [44] 2018 Lea et al [74] Roberts et al [53] Basole et al [63] Sathiyanarayanan et al [68] Haleem et al [45] Saga and Yagi [46] Primary Data as Intentional, Active Digital Collection (CB): The following research conveys geo-location data collected through hardware that is used to track customer be...…”

Section: Customer Behaviour (Cb)mentioning

confidence: 99%

Visualising Business Data: A Survey

Roberts

Laramee

2018

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A rapidly increasing number of businesses rely on visualisation solutions for their data management challenges. This demand stems from an industry-wide shift towards data-driven approaches to decision making and problem-solving. However, there is an overwhelming mass of heterogeneous data collected as a result. The analysis of these data become a critical and challenging part of the business process. Employing visual analysis increases data comprehension thus enabling a wider range of users to interpret the underlying behaviour, as opposed to skilled but expensive data analysts. Widening the reach to an audience with a broader range of backgrounds creates new opportunities for decision making, problem-solving, trend identification, and creative thinking. In this survey, we identify trends in business visualisation and visual analytic literature where visualisation is used to address data challenges and identify areas in which industries use visual design to develop their understanding of the business environment. Our novel classification of literature includes the topics of businesses intelligence, business ecosystem, customer-centric. This survey provides a valuable overview and insight into the business visualisation literature with a novel classification that highlights both mature and less developed research directions.

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Section: Customer Behaviour (Cb)mentioning

confidence: 99%

Visualising Business Data: A Survey

Roberts

Laramee

2018

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“…The SOM methodology does not require data normalization when the analysis has more than 12 dimensions [44], showing great resilience and robustness to multicollinearity problems [45,46].…”

Section: Introduction To Artificial Intelligence Methods Used In Resementioning

confidence: 99%

Urban Shape and Built Density Metrics through the Analysis of European Urban Fabrics Using Artificial Intelligence

2019

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In recent decades, the concept of urban density has been considered key to the creation of sustainable urban fabrics. However, when it comes to measuring the built density, a difficulty has been observed in defining valid measurement indicators universally. With the intention of identifying the variables that allow the best characterization of the shape of urban fabrics and of obtaining the metrics of their density, a multi-variable analysis methodology from the field of artificial intelligence is proposed. The main objective of this paper was to evaluate the capacity and interest of such a methodology from standard indicators of the built density, measured at various urban scales, (i) to cluster differentiated urban profiles in a robust way by assessing the results statistically, and (ii) to obtain the metrics that characterize them with an identity. As a case study, this methodology was applied to the state of the art European urban fabrics (N = 117) by simultaneously integrating 13 regular parameters to qualify urban shape and density. It was verified that the profiles obtained were more robust than those based on a limited number of indicators, evidencing that the proposed methodology offers operational opportunities in urban management by allowing the comparison of a fabric with the identified profiles.

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“…In the research of Saitoh [3], a large sparse matrix can be translated to a small dense matrix using PLSA of a customer review. After this conversion, the arrangement of review sentences is visualized by making a self-organizing map.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Total Quality Management Using CSR Company Reports

Ochikubo

Saitoh

Ishizu

2017

Lecture Notes in Computer Science

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In recent years, serious quality accidents and quality troubles such as recalls have occurred frequently, and Total Quality Management (TQM) is important as effective management to prevent quality troubles beforehand. There is also a Quality Management Level Research [6] (TQM research) that is jointly implemented by the Union of Japanese Scientists and Engineers and the Nikkei. In the TQM survey, the rankings on the six criteria of quality management and comprehensive rankings have been announced. However, concrete TQM activities have not been announced. Meanwhile, Corporate Social Responsibility (CSR) report has published abundant descriptions about the role of customers, employees, society and management who are stakeholders of companies. In this research, we aim to evaluate and extract the characteristics of the company's quality management activities according to the six criteria of the TQM survey using corporate CSR reports.

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Visualization of online customer reviews and evaluations based on Self-organizing Map

Cited by 5 publications

References 12 publications

Visualising Business Data: A Survey

Visualising Business Data: A Survey

Urban Shape and Built Density Metrics through the Analysis of European Urban Fabrics Using Artificial Intelligence

Evaluation of Total Quality Management Using CSR Company Reports

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