What are the most commonly used data mining techniques for identifying patterns in civic complaints?
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Data mining techniques used for identifying patterns in civic complaints include natural language processing (NLP), K-means algorithm, and Bees Algorithm. NLP is employed to identify latent themes in complaints against the police, revealing topics like police effectiveness and misbehavior . In contrast, the K-means and Bees Algorithms are utilized to prioritize urban needs and estimate citizens' satisfaction through clustering methods in the '137 system' for citizen complaints in Iran . These techniques enable the extraction of valuable insights from large sets of complaints, aiding in understanding public sentiments, improving civic engagement, and enhancing social accountability in various contexts, ranging from policing issues to municipal services.
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| Papers (5) | Insight |
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15 Citations | The research paper utilizes K-means and Bees Algorithms (BA) for data mining of citizens' complaints to prioritize urban needs and estimate satisfaction, showing superiority of simultaneous prioritization and satisfaction estimation. |
| The paper focuses on using exploratory spatial data analysis and cluster analysis, particularly K-means algorithm, to identify spatial patterns and typologies in guardianship behavior, not civic complaints. | |
| The paper focuses on panel data analysis, utilizing Ordinary Least Squares (OLS) models to identify how socioeconomic and political factors drive public participation in environmental complaints in Chinese provinces. | |
| The study utilizes topic modeling, a novel computational natural language processing technique, to identify latent themes in citizen complaints against the police, uncovering patterns effectively. | |
| Not addressed in the paper. |
Related Questions
What are the most commonly used data mining techniques for identifying patterns in civic complaints?5 answersThe most commonly used data mining techniques for identifying patterns in civic complaints include Term Frequency–Inverse Document Frequency (TF-IDF), Random Forest (RF), Gradient Boosting (GB), text mining, and Naïve Bayes algorithm. TF-IDF is utilized to create a vector space of complaints, while RF and GB are employed for multi-class text classifications with an accuracy of 73%. Text mining is used to extract major keywords from civil complaints for policy demand identification. The Naïve Bayes algorithm, particularly the ComplementNB model, shows high accuracy in classifying public complaints without resampling data. These techniques aid in automating complaints categorization, improving response times, and enhancing the understanding of citizens' needs and sentiments regarding various civic issues.
How do socio-economic factors contribute to the development of urban complaints?5 answersSocio-economic factors play a significant role in shaping urban complaints. Research indicates that in urban areas like Chicago, where rat infestations are a concern, socio-economic factors influence the reporting of rat complaints. Studies show that the number of public rat complaints can be driven by factors such as household income, urban development levels, and the presence of attractants like uncontained garbage. Additionally, in the context of urban noise pollution, socio-economic factors like population density, building types, and human activity levels impact the likelihood of residents making noise complaints, especially in areas with higher visibility to noise sources. These findings highlight the intricate relationship between socio-economic conditions and the development of urban complaints, emphasizing the need for tailored interventions to address specific urban challenges.
How have digital platforms been utilized in collecting and addressing civic complaints?5 answersDigital platforms have been instrumental in collecting and addressing civic complaints. Government organizations leverage crowdsourcing platforms to engage citizens in designing public services, where the attribution of government responses influences continued participation. Alternative digital democracy platforms like vTaiwan facilitate deliberation on public issues, although limitations exist in algorithmic sorting and participant grouping. Firms increasingly turn to social media for complaint handling, recognizing the benefits of addressing complaints on these platforms. Moreover, advancements in online civic engagement tools have led to the proposal of an integrative online commons, the "Democracy Machine," to enhance sustained and deliberative civic engagement and feedback mechanisms. Civic technologies harness digital tools to promote civic engagement and public decision-making, democratizing participation in civic matters.
What are the most effective methods for utilizing digital platforms to gather and resolve civic complaints?5 answersUtilizing digital platforms for civic complaint management involves various effective methods. Firstly, implementing systems like the proposed software allowing citizens to file complaints with images for transparency. Secondly, employing advanced technologies like multilingual BERT models for automated identification and classification of complaints on platforms like Twitter. Additionally, developing mobile applications based on user acceptance factors can enhance complaint filing and resolution processes. Furthermore, creating digital forums within networking systems can facilitate civic discourse and feedback sharing on various civic issues. Lastly, platforms like CitiCafe with virtual agents and Twitter interfaces can enhance citizen engagement by enabling direct correspondence and information gathering on civic complaints. These methods collectively enhance the efficiency and effectiveness of digital platforms in gathering and resolving civic complaints.
How can i make a complaint process efficient?5 answersTo make a complaint process efficient, several methods can be employed. One approach is to analyze the complaint request and obtain key information, such as identification information and complaint content, to determine if it is an abnormal complaint. Another method involves converting complaint voice into text record information and using a decision tree algorithm to determine the complaint reason with the highest number of votes, which can then be used to provide a corresponding complaint solution. Additionally, text preprocessing and classification can be used to extract sensitive risk words and sentences in complaint texts, allowing for the calculation of emergency degree scores and sorting of the complaints accordingly. Implementing efficient assumptions and guidelines for complaint management can also contribute to an effective and efficient complaint handling process. Finally, by receiving complaint messages and searching for the user identification information of the processing main body, a processing command message can be sent, simplifying the complaint processing process and improving efficiency.
What are the patterns of complaints in healthcare?5 answersComplaints in healthcare can be categorized into several patterns. One common pattern is inadequate communication, where patients and relatives report unsatisfactory information, respect, and empathy. Another pattern is the prevalence of informal complaints, with a significant number of clients reporting causes to complain but only a minority actually submitting formal complaints. Dissatisfaction with "humaneness" and "care/treatment" is another common cause for complaints in hospitals. Complaints also reveal a broader spectrum of harm and suffering beyond formal adverse events, with patients expending considerable effort in persuasive rhetoric to legitimize their complaints. Additionally, the most common complaints include poor attitude and long waiting times, with certain departments, such as pediatrics and emergency medicine, receiving more complaints than others. These patterns highlight the importance of effective communication, addressing patient concerns, and improving service quality in healthcare settings.