An Analysis of Public Complaints to Evaluate Ecosystem Services

Lee, Jae-hyuck; Choi, HaeOk

doi:10.3390/land9030062

Cited by 9 publications

(5 citation statements)

References 63 publications

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“…This technique also identifies distinct characteristics of the area based on the needs of local citizens. Furthermore, combining big data (social networking service data such as Twitter, Facebook, YouTube, and Flickr) [19,20] and regional micro data (in-depth interviews and civil complaints) [21,22] would enable the prioritization of land-use policies specialized for each location. When determining such priorities, spatial text mining could provide important data that represents the needs and opinions of local residents.…”

Section: Discussionmentioning

confidence: 99%

Mapping Environmental Conflicts Using Spatial Text Mining

Lee

Kim

2020

Land

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Mapping the characteristics and extent of environmental conflicts related to land use is important for developing regionally specific policies. However, because it is only possible to verify the frequency of conflicts on a specific predetermined subject, it is difficult to determine the various reasons for conflicts in a region. Therefore, this study mapped the current status of regional environmental conflicts in South Korea using a spatial text mining technique, then proposed relevant management policies. The results were obtained by analyzing environmental conflict data extracted from the online agendas of regional environmental organizations. Air quality-related conflicts in South Korea are concentrated in western municipalities; development-related conflicts are concentrated in the southern region of Jeju Island; and intensive safety-related conflicts occur in metropolitan areas, particularly Ulsan. Thus, the type of conflict is determined by the local environment, in accordance with the definition of environmental conflict, and the distribution is determined by the location of the stakeholder population. This study reveals the issues and locations related to local environmental conflict that require further attention, and proposes more wide-ranging methods for managing the links between conflicts by mapping environmental conflicts on a large scale rather than on an individual basis.

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Section: Discussionmentioning

confidence: 99%

Mapping Environmental Conflicts Using Spatial Text Mining

Lee

Kim

2020

Land

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“…Text mining produces quantified numbers, which can be used for mapping. This method has been defined as ‘spatial text mining’ 9 , 25 . Through a factor analysis between spaces and significant words, this technique can identify the main keywords used in each response and the characteristics of each distribution.…”

Section: Literature Reviewsmentioning

confidence: 99%

Ecosystem service evaluation based on local knowledge of residents using spatial text-mining

Lee,

Ahn

2023

Sci Rep

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This study aims to evaluate the ecosystem services of Upo wetland, one of the best-known Ramsar sites in Korea, reflecting the characteristics of the ecological assets and local knowledges in the area. Application of spatial text-mining begins with collecting local perceptions and knowledge of residents on the 17 ecological assets of Upo site and surrounding area. Our results identified five important ecosystem services: flood control during heavy rainfall, water purification by aquatic plants, cultural and natural heritages, agricultural products and water provision for crop cultivation. GIS created a map where these ecosystem services were linked to the locations of 17 ecological assets. This map showed which ecosystem service is associated with particular ecological assets and their characteristics from residents’ perspectives. Mapping local knowledge using the spatial text-mining is able to identify multi-functional bases which provide various ecosystem services in the same location simultaneously. Identification of multi-functional bases can provide information for local government to design an effective and comprehensive management plan considering physical-cultural geography of ecosystem services.

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“…In other study, Chen et al (2021) identified the affecting factors of customers satisfaction from unstructured online comments by lessening personal communication to collect these reviews. Lee and Choi (2020) studied public environmental complaints to investigate the factors that contribute to reducing ecosystem benefits. They performed statistical and spatial analyses on the complaints received by the Namyangju government.…”

Section: Related Workmentioning

confidence: 99%

Categorization of Customer Complaints in Food Industry Using Machine Learning Approaches

Bozyiğit¹,

Doğan²,

Kılınç³

2022

Journal of Intelligent Systems: Theory and Applications

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Customer feedback is one of the most critical parameters that determine the market dynamics of product development. In this direction, analyzing product-related complaints helps sellers to identify the quality characteristics and consumer focus. There have been many studies conducted on the design of Machine Learning (ML) systems to address the causes of customer dissatisfaction. However, most of the research has been particularly performed on English. This paper contributes to developing an accurate categorization of customer complaints about package food products, written in Turkish. Accordingly, various ML algorithms using TF-IDF and word2vec feature representation strategies were performed to determine the category of complaints. Corresponding results of Linear Regression (LR), Naive Bayes (NB), k Nearest Neighbour (kNN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) classifiers were provided in related sections. Experimental results show that the best-performing method is XGBoost with TF-IDF weighting scheme and it achieves %86 F-measure score. The other considerable point is word2vec based ML classifiers show poor performance in terms of F-measure compared to the TF-IDF term weighting scheme. It is also observed that each experimented TF-IDF based ML algorithm gives a more successful prediction performance on the optimal subsets of features selected by the Chi Square (CH2) method. Performing CH2 on TF-IDF features increases the F-measure score from 86% to 88% in XGBoost.

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An Analysis of Public Complaints to Evaluate Ecosystem Services

Cited by 9 publications

References 63 publications

Mapping Environmental Conflicts Using Spatial Text Mining

Mapping Environmental Conflicts Using Spatial Text Mining

Ecosystem service evaluation based on local knowledge of residents using spatial text-mining

Categorization of Customer Complaints in Food Industry Using Machine Learning Approaches

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