## 2 Literature Review
### AI for Complaint Management and Pattern Recognition  
A particularly relevant application of AI in public administration is the handling of citizen complaints and feedback, especially through the analysis of textual data and user interaction logs. Government agencies often collect large volumes of complaints, service requests, and feedback (for example, through 311 systems, online portals, or social media). Pattern recognition techniques in AI, notably natural language processing (NLP) and text mining, have been leveraged to make sense of these massive datasets. Multiple studies have applied text mining to public complaint records to \"_analyze complaints, gain insights and identify prominent issues_\" emerging from the public [25]. By automatically categorizing and prioritizing complaints, AI can help administrators spot recurrent problems or systemic issues that may require managerial attention. One practical use-case is automatic complaint classification and routing. Rather than relying solely on staff to read and forward each complaint to the right department, machine learning classifiers can be trained to recognize the topic of a complaint (e.g., garbage collection, road maintenance, IT service glitch) and route it to the responsible unit. For instance, [PERSON] et al. (2017) developed a text classification system for a city's e-complaint center that assigns incoming complaints to the appropriate municipal department, significantly speeding up response times [26]. In recent years, such approaches have become more sophisticated, using advanced NLP and even deep learning. These systems learn from historical complaint data and can continually improve their accuracy in triaging issues. This is highly relevant for informatics-related complaints (such as IT service complaints in a public agency), where AI can quickly group similar incident reports and flag if a particular system is generating many trouble tickets. Beyond classification, sentiment analysis is another AI tool used for citizen feedback. Sentiment analysis algorithms assess whether the text of a complaint or comment is positive,negative, or neutral in tone. Public managers can use this to gauge public sentiment about specific services or initiatives. For example, if there is a surge of negative sentiment in feedback about a new digital service, it may indicate usability problems. One IEEE study compared multiple machine learning models (SVM, Naive Bayes, Random Forest, XGBoost) for sentiment analysis of public complaints, illustrating growing interest in identifying the emotional tone and satisfaction level expressed by citizens [26]. Knowing not just _what_ issues people report but also _how_ they feel about them provides a deeper understanding of public perception. AI-driven pattern recognition extends to analyzing user interaction logs from government digital platforms. Every click or action users take on a government website can be logged as data. By mining these logs, agencies can detect patterns such as where users encounter difficulties (e.g., dropping off at a certain page) or which services have unusually high error rates. While academic literature specifically focusing on user interaction log analysis in public administration is still emerging, parallels can be drawn from e-commerce and other domains where such analysis is common. In the public sector, these methods can translate to improvements like redesigning a form that many users struggle with or identifying cyber-security anomalies in log data. For example, anomaly detection algorithms could sift through IT system logs in real time to catch suspicious activities or failures, enabling quicker complaint resolution for informatics issues. Recent advancements incorporate deep learning and transformer-based models for enhanced accuracy in topic modeling and ticket triage. A study explored Top2 Vec, a transformer-topic framework combining word embeddings and clustering, to analyze public complaints about urban services.