This study presents WordMap, an integrated text mining application developed to enhance the efficiency and usability of text analysis over a network. As unstructured text data continues to grow across domains, effective tools for segmentation and topic modeling have become increasingly essential for extracting insightful information. However, most existing solutions depend on multiple disconnected tools, and these often compromise workflow efficiency and user experience. Unlike traditional tools, WordMap combines corpus segmentation, topic modeling, and result visualization into a unified workflow for both Chinese and English languages, thereby reducing workflow fragmentation and lowering the user threshold. To assess usability and user acceptance, this research adopts the Technology Acceptance Model (TAM). WordMap employs PKUSEG and NLTK for bilingual corpus segmentation, utilizes BERTopic for dynamic topic modeling, and integrates interactive visualization to enable intuitive analysis. The PLS-SEM result shows that the perceived ease of use (PEOU) has a significant impact on both perceived usefulness (PU) and user attitude (ATT), while ATT strongly predicts behavioral intention (BI) (β = 0.674, p < 0.001). The results indicate that integrating core text mining processes into a user-centered design significantly boosts user satisfaction and adoption. By combining key processes and empirically validating user perceptions, the proposed framework facilitates the development of efficient and accessible text mining tools. It offers both theoretical and practical insights for future advancement and deployment in the field of text mining.

This study examines toponymic dissonance, the systematic semantic asymmetry and translational non-equivalence between Chinese (Cantonese) and Portuguese street names in Macau, as a case of post-colonial heritage governance operating under the “One Country, Two Systems” framework. Drawing on 1255 streets documented in Macau’s official street registries, the study is situated at the intersection of translation studies, critical toponymy, linguistic landscape research, post-colonial urban studies, and heritage governance. The analysis works from two classificatory instruments: (i) a four-dimensional coding system distinguishing anthroponymic, toponymic, functional/descriptive, and abstract/idealistic naming sources; (ii) a four-tier semantic distance evaluation (Levels 0–3) that quantifies the divergence between the official Portuguese name and the locally negotiated Cantonese name. Empirically, 439 of 1255 streets (35.0%) exhibit “complete asymmetry” (Level 3), wherein Chinese and Portuguese names derive from irreconcilably different semantic sources; this paper identifies five principal types of asymmetry, ranging from political commemoration versus functional or geographic description to the de-institutionalisation of colonial administrative references. Crucially, the study also engages the contemporary multilingualisation of Macau’s linguistic landscape, in which English, Tagalog, Bahasa Indonesia, Burmese, Hindi, and Nepali are increasingly visible alongside Chinese and Portuguese, and traces the post-1999 institutional decision to preserve semantic asymmetry as a deliberate governance strategy: bilingual street signs perform a “Luso-Chinese cosmopolitan” identity that is now economically indispensable to Macau’s tourism and gaming-led economy, institutionally aligned with the Macau Forum and the Portuguese-speaking Countries network, and politically differentiated from the colonial-era renaming strategies seen in Hong Kong and other formerly colonised cities. Drawing upon Shi-xu’s cultural discourse theory, the paper argues that Macau’s bilingual street names constitute a “transcultural cultural field” in which national power, local memory, and international image interact continuously, and that the post-1999 preservation of asymmetry is best understood as a technique of identity management rather than as a passive inheritance of colonial habit. By offering this interdisciplinary synthesis, the paper advances a comparative resource for the study of multilingual cities in the Global East and elsewhere where colonial, national, and local discourses continue to negotiate the urban landscape.

In dermatological medical image analysis, edge detection is critical for skin disease diagnosis. Precise edge information provides a vital basis for subsequent disease assessment. However, existing grayscale gradient-based edge detection methods exhibit poor visual effects when extracting features of complex skin textures, often due to interference from noise and inability to obtain complete, clear edge images, while deep learning-based methods suffer from heavy reliance on large annotated datasets and high computing costs. To improve the visual quality of edge detection for skin images and extract clear, complete skin edges, we propose the training-free, lightweight Skin Lesion Contour Detection (SLCD) method. It integrates Haar wavelet transform denoising and 3 × 3 circular structuring element morphological processing for two-stage preprocessing of skin images. The processed image uses 3 Most Significant Bit (MSB) planes, and edge detection is performed on each bit plane separately. Each bit plane is processed by the 3 × 3 neighborhood adaptive analysis based on local standard deviation and average gradient to get the edge image, and the resulting edge images are fused to generate the final detection result. Experiments on the Skin Image Edge Detection Test (SIEDT) dataset show that SLCD achieves an average F1-score of 0.718, Jaccard index of 0.575, and Matthews Correlation Coefficient (MCC) of 0.733. All of which are the best among the compared methods. The SLCD can effectively extract true skin edges and mitigate noise interference in skin images, providing a training-free and lightweight alternative that offers better edge detection performance than traditional operators while avoiding the heavy data and computational requirements of deep learning models in resource-limited skin image analysis scenarios. This study provides multi-bitplane fusion-based novel concepts and methods for edge detection in skin images. It delivers diagnostic value by providing high-precision lesion contours, supporting analysis of skin lesions, early diagnosis, and surgical planning, and improving the reliability of clinical decision-making in dermatology.