User Profile & Attitude Analysis Based on Unstructured Social Media and Online Activity

As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.

Sensitivity analysis for stochastic user equilibrium with elastic demand assignment model

This paper puts forward a rigorous approach for a sensitivity analysis of stochastic user equilibrium with the elastic demand （SUEED） model. First, proof is given for the existence of derivatives of output variables with respect to the perturbation parameters for the SUEED model. Then by taking advantage of the gradient-based method for sensitivity analysis of a general nonlinear program, detailed formulae are developed for calculating the derivatives of designed variables with respect to perturbation parameters at the equilibrium state of the SUEED model. This method is not only applicable for a sensitivity analysis of the logit-type SUEED problem, but also for the probit-type SUEED problem. The application of the proposed method in a numerical example shows that the proposed method can be used to approximate the equilibrium link flow solutions for both logit-type SUEED and probit-type SUEED problems when small perturbations are introduced in the input parameters.

The Research on E-mail Users' Behavior of Participating in Subjects Based on Social Network Analysis

The e-mail network is a type of social network. This study analyzes user behavior in e-mail subject participation in organizations by using social network analysis. First, the Enron dataset and the position-related information of an employee are introduced, and methods for deletion of false data are presented. Next, the three-layer model(User, Subject, Keyword) is proposed for analysis of user behavior. Then, the proposed keyword selection algorithm based on a greedy approach, and the influence and propagation of an e-mail subject are defined. Finally, the e-mail user behavior is analyzed for the Enron organization. This study has considerable significance in subject recommendation and character recognition.

Computing the User Experience via Big Data Analysis:A Case of Uber Services

As of 2020,the issue of user satisfaction has generated a significant amount of interest.Therefore,we employ a big data approach for exploring user satisfaction among Uber users.We develop a research model of user satisfaction by expanding the list of user experience(UX)elements(i.e.,pragmatic,expectation confirmation,hedonic,and burden)by including more elements,namely:risk,cost,promotion,anxiety,sadness,and anger.Subsequently,we collect 125,768 comments from online reviews of Uber services and perform a sentiment analysis to extract the UX elements.The results of a regression analysis reveal the following:hedonic,promotion,and pragmatic significantly and positively affect user satisfaction,while burden,cost,and risk have a substantial negative influence.However,the influence of expectation confirmation on user satisfaction is not supported.Moreover,sadness,anxiety,and anger are positively related to the perceived risk of users.Compared with sadness and anxiety,anger has a more important role in increasing the perceived burden of users.Based on these findings,we also provide some theoretical implications for future UX literature and some core suggestions related to establishing strategies for Uber and similar services.The proposed big data approach may be utilized in other UX studies in the future.

Aspect-Level Sentiment Analysis Based on Deep Learning

In recent years,deep learning methods have developed rapidly and found application in many fields,including natural language processing.In the field of aspect-level sentiment analysis,deep learning methods can also greatly improve the performance of models.However,previous studies did not take into account the relationship between user feature extraction and contextual terms.To address this issue,we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis method.To be specific,we design user comment feature extraction(UCFE)to distill salient features from users’historical comments and transform them into representative user feature vectors.Then,the aspect-sentence graph convolutional neural network(ASGCN)is used to incorporate innovative techniques for calculating adjacency matrices;meanwhile,ASGCN emphasizes capturing nuanced semantics within relationships among aspect words and syntactic dependency types.Afterward,three embedding methods are devised to embed the user feature vector into the ASGCN model.The empirical validations verify the effectiveness of these models,consistently surpassing conventional benchmarks and reaffirming the indispensable role of deep learning in advancing sentiment analysis methodologies.

User Profile System Based on Sentiment Analysis for Mobile Edge Computing

Emotions of users do not converge in a single application but are scattered across diverse applications.Mobile devices are the closest media for handling user data and these devices have the advantage of integrating private user information and emotions spread over different applications.In this paper,we first analyze user profile on a mobile device by describing the problem of the user sentiment profile system in terms of data granularity,media diversity,and server-side solution.Fine-grained data requires additional data and structural analysis in mobile devices.Media diversity requires standard parameters to integrate user data from various applications.A server-side solution presents a potential risk when handling individual privacy information.Therefore,in order to overcome these problems,we propose a general-purposed user profile system based on sentiment analysis that extracts individual emotional preferences by comparing the difference between public and individual data based on particular features.The proposed system is built based on a sentiment hierarchy,which is created by using unstructured data on mobile devices.It can compensate for the concentration of single media,and analyze individual private data without the invasion of privacy on mobile devices.

Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

Different types of pandemics that have appeared from time to time have changed many aspects of daily life.Some governments encourage their citizens to use certain applications to help control the spread of disease and to deliver other services during lockdown.The Saudi government has launched several mobile apps to control the pandemic and have made these apps available through Google Play and the app store.A huge number of reviews are written daily by users to express their opinions,which include significant information to improve these applications.The manual processing and extracting of information from users’reviews is an extremely difficult and time-consuming task.Therefore,the use of intelligent methods is necessary to analyse users’reviews and extract issues that can help in improving these apps.This research aims to support the efforts made by the Saudi government for its citizens and residents by analysing the opinions of people in Saudi Arabia that can be found as reviews on Google Play and the app store using sentiment analysis and machine learning methods.To the best of our knowledge,this is the first study to explore users’opinions about governmental apps in Saudi Arabia.The findings of this analysis will help government officers make the right decisions to improve the quality of the provided services and help application developers improve these applications by fixing potential issues that cannot be identified during application testing phases.A new dataset used for this research includes 8000 user reviews gathered from social media,Google Play and the app store.Different methods are applied to the dataset,and the results show that the k nearest neighbourhood(KNN)method generates the highest accuracy compared to other implemented methods.

Enhancing Data Analysis and Automation: Integrating Python with Microsoft Excel for Non-Programmers

Microsoft Excel is essential for the End-User Approach (EUA), offering versatility in data organization, analysis, and visualization, as well as widespread accessibility. It fosters collaboration and informed decision-making across diverse domains. Conversely, Python is indispensable for professional programming due to its versatility, readability, extensive libraries, and robust community support. It enables efficient development, advanced data analysis, data mining, and automation, catering to diverse industries and applications. However, one primary issue when using Microsoft Excel with Python libraries is compatibility and interoperability. While Excel is a widely used tool for data storage and analysis, it may not seamlessly integrate with Python libraries, leading to challenges in reading and writing data, especially in complex or large datasets. Additionally, manipulating Excel files with Python may not always preserve formatting or formulas accurately, potentially affecting data integrity. Moreover, dependency on Excel’s graphical user interface (GUI) for automation can limit scalability and reproducibility compared to Python’s scripting capabilities. This paper covers the integration solution of empowering non-programmers to leverage Python’s capabilities within the familiar Excel environment. This enables users to perform advanced data analysis and automation tasks without requiring extensive programming knowledge. Based on Soliciting feedback from non-programmers who have tested the integration solution, the case study shows how the solution evaluates the ease of implementation, performance, and compatibility of Python with Excel versions.

User Behavior Path Analysis Based on Sales Data

With the rapid development of science and technology and the increasing popularity of the Internet,the number of network users is gradually expanding,and the behavior of network users is becoming more and more complex.Users’actual demand for resources on the network application platform is closely related to their historical behavior records.Therefore,it is very important to analyze the user behavior path conversion rate.Therefore,this paper analyses and studies user behavior path based on sales data.Through analyzing the user quality of the website as well as the user’s repurchase rate,repurchase rate and retention rate in the website,we can get some user habits and use the data to guide the website optimization.

Research on Association Analysis Technology of Network User Accounts

As large numbers of user information on various network services are becoming valuable resources for social computing,how to associate user information scattered in a variety of services turns to one of the key issues of user identity mining.First this paper outlines the difficulties of dealing with account association,then describes the basis of account association method.After that,this paper sums up account association methods mainly from five aspects which are user naming conventions,user profiles,user writing styles,user online behavior,and user community relationship.Finally,this paper points out the development direction and prospects of account association.

Task analysis approach based on perceptual control theory

Based on perceptual control theory,a task analysis approach is proposed to describe more accurately user tasks in dynamic environments,which is of more powerful and flexible descriptive ability. Theoretically,a task meta model is established to describe the interactive process in an individual,dynamic,and flexible way.Methodologically,an implementation framework is illustrated to map the user-oriented description into implementation-oriented models,which will be as a technical tool to transform from a task model to a user interface prototype.

Analysis of Land Use and Land Cover Changes and Evaluation of Natural Generation and Potential Restoration Areas in the Mexican Huasteca Region

This study aims to analyze temporal changes in land use and land cover change (LUCC) as well as identify areas for natural regeneration and potential areas for forest restoration in the Huasteca region for the period from 1976 to 2007. Changes were quantified in numbers and, additionally, cartography was used to identify and map the main affected areas. Different models based on Geographic Information Systems (GIS) demonstrated that LUCC have occurred on an area of 11718.82 km2, representing 17.84% of the region’s surface. Agriculture and the growth of pasture could be identified as the main human-induced activities that have led to landscape modification. In addition, forest cover is affected by a deforestation rate which is higher than the national average. Further important changes include a change from natural land cover to non-original land cover, affecting an area of 4911.88 km2 in the period from 1976-1993, and an area of 1892.5 km2 in the period from 1993-2002. Smaller changes could be observed for the period from 1993-2002 with an affected area of 1029.78 km2. At the same time, a natural regeneration from nonoriginal to original land cover took place from 1976-1993 on an area of 1318.68 km2, and also on an area of 974.18 km2 between 1993 an 2002. The surface that underwent a natural regeneration of forest cover made up 1932.07 km2. At the same time, an area of 5739.29 km2 for potential forest restoration was identified. Drawing on GIS methods and techniques, the development of thematic maps for land use, land use and land cover changes for the years of analysis (1976-1993-2002-2007) proved to be very adequate for the evaluation and analysis of the land cover and land use change, in particular with regard to the decrease of natural vegetation cover.

Design of a Tree Ring Structure Analysis System to Estimate the Accurate Age of Tree Species in Sri Lanka

Determination of an age in a particular tree species can be considered as a vital factor in forest management.In this research we have introduced a novel scheme to determine the accurate age of the tree species in Sri Lanka.This is initially developed for the tree species called‘Hora’(Dipterocarpus zeylanicus)in wet zone of Sri Lanka.Here the core samples are extracted and further analyzed by means of the different image processing techniques such as Gaussian kernel blurring,use of Sobel filters,double threshold analysis,Hough line tran sformation and etc.The operations such as rescaling,slicing and measuring are also used in line with image processing techniques to achieve the desired results.Ultimately a Graphical user interface(GUI)is developed to cater for the user requirements in a user friendly environment.It has been found that the average growth ring identification accuracy of the proposed system is 93%and the overall average accuracy of detecting the age is 81%.Ultimately the proposed system will provide an insight and contributes to the forestry related activities and researches in Sri Lanka.

An Educational GUI-Based Software for Dynamic Analysis of Framed Structural Models

This paper introduces a new version of the open-source educational software, LESM (Linear Elements Structure Model), developed in MATLAB for structural analysis of one-dimensional models such as frames, trusses, and grillages. The updated program includes dynamic analysis, which incorporates inertial and damping effects, time-dependent load conditions, and a transient solver with multiple time integration schemes. The software assumes small displacements and linear-elastic material behavior. The paper briefly explains the theoretical basis for these developments and the reorganization of the source code using Object-Oriented Programming (OOP). The updated Graphical User Interface (GUI) allows interactive use of dynamic analysis features and displays new results such as animations, envelope diagrams of internal forces, phase portraits, and the response of degrees-of-freedom in time and frequency domain. The new version was used in a structural dynamics course, and new assignments were elaborated to improve students’ understanding of the subject.

User-oriented web search based on PLSA

In order to solve the problem that current search engines provide query-oriented searches rather than user-oriented ones, and that this improper orientation leads to the search engines＇ inability to meet the personalized requirements of users, a novel method based on probabilistic latent semantic analysis （PLSA） is proposed to convert query-oriented web search to user-oriented web search. First, a user profile represented as a user＇ s topics of interest vector is created by analyzing the user＇ s click through data based on PLSA, then the user＇ s queries are mapped into categories based on the user＇ s preferences, and finally the result list is re-ranked according to the user＇ s interests based on the new proposed method named user-oriented PageRank （UOPR）. Experiments on real life datasets show that the user-oriented search system that adopts PLSA takes considerable consideration of user preferences and better satisfies a user＇ s personalized information needs.

Changes in the default mode network in the prefrontal lobe, posterior cingulated cortex and hippocampus of heroin users

The default mode network is associated with senior cognitive functions in humans. In this study, we performed independent component analysis of blood oxygenation signals from 14 heroin users and 13 matched normal controls in the resting state through functional MRI scans. Results showed that the default mode network was significantly activated in the prefrontal lobe, posterior cingulated cortex and hippocampus of heroin users, and an enhanced activation signal was observed in the right inferior parietal Iobule （P 〈 0.05, corrected for false discovery rate）. Experimental findings indicate that the default mode network is altered in heroin users.

Modelling User Needs:Students as Enterprise Analysts

We illustrate a case study,where students designed enterprise architectures,that were not only welcome but successfully implemented.The success key was threefold.First the analysis framework,that integrates all the aspects of the systems that are relevant to users,namely user interface,rules,and information.Second,the analysis approach,that guides,trough confirmatory sessions,to elicit real requirements from users.Third,the model-to-model transformation,that assures consistency from the highest aggregate abstraction down to an executable model.

Exploring tourism networks in the Guangxi mountainous area using mobility data from user generated content

Tourism-led economic growth and tourism-driven urbanization have attracted increasing attention by provinces and regions in China with abundant tourism resources.Due to low data availability,the current tourism literature lacks empirical evidence of the tourism network in lessdeveloped mountainous regions where the development of transport infrastructure is more variable.This paper aims to provide such evidence using Guangxi Zhuang Autonomous Region in China as a case study.Using User Generated Content(UGC)data,this study constructs a tourism network in Guangxi.By integrating social network analysis with spatial interaction modelling,we compared the impact of two different transport infrastructures,highway and high-speed railway,on tourist flows,particularly in less-developed mountainous regions.It was found that the product of node centrality and flow could best describe the significant pushing and pulling forces on the flow of tourists.The tourism by high-speed railway was sensitive to the position of trip destination on the whole tourism network but self-drive tourism was more sensitive to travelling time.The increase of high-speed railway density is crucial to promote local tourism-led economic development,however,large-scale karst landforms in the study area present a significant obstacle to the construction of high-speed railways.

Exploring users' within-site navigation behavior:A case study based on clickstream data

Purpose:The goal of our research is to suggest specific Web metrics that are useful for evaluating and improving user navigation experience on informational websites.Design/methodology/approach:We revised metrics in a Web forensic framework proposed in the literature and defined the metrics of footprint,track and movement.Data were obtained from user clickstreams provided by a real estate site’s administrators.There were two phases of data analysis with the first phase on navigation behavior based on user footprints and tracks,and the second phase on navigational transition patterns based on user movements.Findings:Preliminary results suggest that the apartment pages were heavily-trafficked while the agent pages and related information pages were underused to a great extent.Navigation within the same category of pages was prevalent,especially when users navigated among the regional apartment listings.However,navigation of these pages was found to be inefficient.Research limitations:The suggestions for navigation design optimization provided in the paper are specific to this website,and their applicability to other online environments needs to be verified.Preference predications or personal recommendations are not made during the current stage of research.Practical implications:Our clickstream data analysis results offer a base for future research.Meanwhile,website administrators and managers can make better use of the readily available clickstream data to evaluate the effectiveness and efficiency of their site navigation design.Originality/value:Our empirical study is valuable to those seeking analysis metrics for evaluating and improving user navigation experience on informational websites based on clickstream data.Our attempts to analyze the log file in terms of footprint,track and movement will enrich the utilization of such trace data to engender a deeper understanding of users’within-site navigation behavior.

Wanting a Life in Decency!—A Qualitative Study from Experienced Electric Wheelchairs Users’ perspective

Background: The functionality and the safety of the electric wheelchairs were essential for users’ everyday life. Some evidence indicated that the wheelchair Per Se highly influenced users’ occupational life, their personal identity and social life;further, the wheelchair became an extension of the body and more than a technical device. Besides, there was still both environmental and self-efficacy or/and mental health factors obstacles for full social participation. Even so, there was to some extent stigma related to being a wheelchair user. There was a need to reflect users’ perspective on being depended on electric wheelchair. The aim, accordingly, was to describe and to get a deeper insight into electric wheelchairs users’ perspective and experiences of utilizing this device;a qualitative design with an inductive approach was used. Method: Qualitative latent and interpretative content analysis [1] [2] was used after repeated face-to-face semi-structured interviews with three experienced Swedish electric wheelchair users during the autumn 2017. Findings: The findings showed a high degree of dependability of the assistants that supported the users, and of the quality of that working relationship. The findings were formulated, abstracted and interpreted in several steps. It showed one theme of meaning: “Living in a space shifting between potential violation of or respect for human dignity”. Conclusion: The study showed that electric wheelchair users were relatively content with their lives as well as with their devices in turns of mobility and accessibility, but the meaning of their narrations showed a life at constant risk of having the respect of human rights and human respect violated. Besides, the importance of having access to good and high quality devices, good staffing, and environmental support, all in concordance with human rights, the clinical and practical implications of this study narrows down to a question of encountering the other person as a whole and worthy individual.