Understanding User Experience and Satisfaction with Urban Infrastructure through Text Mining of Civil Complaint Data

Chang, Taeyeon; Chi, Seokho; Im, Seok-Been

doi:10.1061/(asce)co.1943-7862.0002308

Cited by 9 publications

(8 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A few studies have undertaken to increase service quality and user satisfaction with public infrastructure through effective infrastructure maintenance [12][13][14]. Chang et al [15] recently sought to better understand user satisfaction/dissatisfaction factors by mining the contents of user complaints regarding tunnel and bridge infrastructure.…”

Section: Literature Review 21 Related Work On Occupant Satisfaction A...mentioning

confidence: 99%

“…The main objective of text pre-processing is to convert the collected data in unstructured text format into meaningful terms for SNA purposes [15,45,52]. Toward this objective, the text pre-processing in the present study consisted of cleaning and normalization, tokenization, morpheme analysis, and removal of stopwords.…”

Section: Text Pre-processingmentioning

confidence: 99%

“…In the third step, morphological analysis was implemented to identify and extract important terms related to occupant dissatisfaction from the tokenized data. In general, in Korean, nouns are the words that contain the critical information regarding occupants' experiences and satisfaction/dissatisfaction [15]. However, adjectives (e.g., inadequate, unkind) and verbs (e.g., repair, promise) also contain some important information in this regard.…”

Section: Identification Of Occupant Dissatisfaction Factorsmentioning

confidence: 99%

“…In other words, a word with a high TF count is considered more important than words with lower TF counts in the given dataset. Due to its straightforward and intuitive calculation process, many studies have adopted TF for extracting keywords from text data [12,15]. However, TF may not be a reliable method for measuring how important a term is within a text relative to the entire text dataset [53].…”

Section: Keyword Extractionmentioning

confidence: 99%

“…Construction defect disputes often arise from occupant dissatisfaction caused by miscommunication between the construction company and the occupant, or by poor-quality construction defect repair (e.g., noncompliance with occupants' requests). In an effort to improve both service quality and occupant satisfaction, researchers have explored occupant dissatisfaction factors based on occupant complaint data for such facilities and infrastructure as buildings, bridges, water distribution systems, and metro systems [12][13][14][15]. Assaf and Srour [16], in a study seeking to develop a building maintenance strategy to enhance occupant satisfaction and building performance, proposed a neural network approach to predict building occupants' complaints.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Identification of Occupant Dissatisfaction Factors in Newly Constructed Apartments: Text Mining and Semantic Network Analysis

Noh,

Jo,

Han

et al. 2023

Buildings

View full text Add to dashboard Cite

With apartment buildings representing a rapidly growing share of the residential market in South Korea, the effect of construction defects throughout the life cycle of construction projects, and particularly during the occupancy stage, has emerged as a significant social issue that may ultimately lead to an increase in defect disputes between new occupants and general contractors. An important step toward mitigating the likelihood of these defect disputes is to identify and address the factors that give rise to occupant dissatisfaction during the defect repair process. However, a reliable method by which to identify these factors has yet to be developed. In this respect, the main objective of the research presented in this paper is to develop a method for identifying occupant dissatisfaction factors in the construction defect repair stage. The developed method comprises the following procedures: (i) text pre-processing, which involves data cleaning, normalization, tokenization, morphological analysis, and removal of stopwords; (ii) term frequency–inverse document frequency for keyword extraction; and (iii) semantic network analysis to recognize relationships between words. The method was implemented using a dataset of 12,874 comments in Korean text format obtained from apartment building occupants. Based on the processing and analysis of this dataset, the occupant dissatisfaction factors were found to be: (i) inaccurate and inadequate repair work (represented by such keywords as “Repair”, “Visit”, and “Accuracy”); (ii) failure to keep promises (e.g., “Fulfillment”, “Promise”, and “Change”); and (iii) unprofessional conduct on the part of representatives in the repair service center (e.g., “Response”, “Attitude”, and “Receipt”).

show abstract