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Follower Behavior Analysis via Influential Transmitters on Social Issues in Twitter

Kwang-Yong Jeong, Kyung-Soon Lee
2016 Journal of Computacion y Sistemas  
The thread tweets are clustered based on Latent Dirichlet Allocation for social issues. Then, sentiment analysis is conducted for the clusters of a follower.  ...  To detect a follower's specific opinion, social issues are extracted based on tweets of influential transmitters.  ...  Acknowledgements Follower Behavior Analysis via Influential Transmitters on Social Issues in Twitter 423 Center) support program (IITP-2016-R0992-15-1023) supervised by the IITP (Institute for Information  ... 
doi:10.13053/cys-20-3-2452 fatcat:hlmyrtmxkrharhroabxzqq2wb4

Knowledge Discovery from Dynamically Evolving User Profiles

Md. AhemadPasha, R Vijaya Prakash
2014 International Journal of Computer Applications  
In this paper it analyzes computer user behavior based on the commands that they execute as part of their job profile.  ...  For this reason the user behavior profiles are continually updated on order to ensure that they reflect the true knowledge of the behavior of users with respect to their job roles.  ...  The user behavior can be used to take necessary steps based on the result of analysis.  ... 
doi:10.5120/14805-3013 fatcat:tvfzgu7kdfdmlnzdlpr5dk67ou

An anomaly-based approach to the analysis of the social behavior of VoIP users

S. Chiappetta, C. Mazzariello, R. Presta, S.P. Romano
2013 Computer Networks  
The objective of such study was twofold: (i) first of all, we wanted to be able to tell well-behaving users apart from potential malicious ones; (ii) once done with the first, coarse-grained, classification  ...  The paper shows how we can reliably identify behavioral patterns associated with the most common anomalous behaviors of VoIP users.  ...  clustering algorithm, capable to provide a first visual representation of the classification made by the clustering algorithm; (iii) chart of the time distribution of the calls issued by a suspect user  ... 
doi:10.1016/j.comnet.2013.02.009 fatcat:dysjgzkssbh2na6forpxwecxme

Context-aware prediction of user's first click

Liang Wu, Alvin Chin, Yuanchun Zhou, Xia Wang, Kangjian Meng, Yonggang Guo, Jianhui Li
2012 Proceedings of the 1st International Workshop on Context Discovery and Data Mining - ContextDD '12  
Location-based services has attracted attentions from both industry and academia.  ...  Our proposed approach models the problem as a multi-label classification. We introduce three sets of features including location feature, time feature and behavioral feature.  ...  Item-based CF algorithms quantify a product's characteristics based on the group of users who purchased it. A challenging issue of CF-based models is how to conquer the cold start problem.  ... 
doi:10.1145/2346604.2346613 fatcat:wmeeftkbbjayjjktg3do2yb4me

Railway Infrastructure and Traveller usage Prediction and Rendering Solutions

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Thus our proposed system involves data collection of the users based on id, username, gender, age, the timing of travel, station source and destination to monitor the user travel behavior.  ...  For this work, In R programming, we use K-means algorithm for clustering and use Naive Bayes algorithm for machine learning and solution defining.  ...  We have created an user interface in this module where user can input his choice for clustering and classification .  ... 
doi:10.35940/ijitee.j9296.1081219 fatcat:c3t7ijjh6ffcbl6fbvvesfxxbu

On Modeling Community Behaviors and Sentiments in Microblogging [chapter]

Tuan-Anh Hoang, William W. Cohen, Ee-Peng Lim
2014 Proceedings of the 2014 SIAM International Conference on Data Mining  
In this paper, we propose the CBS topic model, a probabilistic graphical model, to derive the user communities in microblogging networks based on the sentiments they express on their generated content  ...  Our experiments on two Twitter datasets show that the CBS model can effectively mine the representative behaviors and emotional topics for each community.  ...  80 for the user classification task.  ... 
doi:10.1137/1.9781611973440.55 dblp:conf/sdm/HoangCL14 fatcat:hitjgzrrmzcihpj6itvor2zjai

Recommendations of Personal Web Pages Based on User Navigational Patterns

Yin-Fu Huang, Jia-Tang Jhang
2014 International Journal of Machine Learning and Computing  
These navigational patterns are then used to generate recommendation web pages by matching the navigation behavior of a user personal knowledge base.  ...  The experimental results show that the web pages recommended by our system are of better quality and acceptable for humans from various domains, based on human evaluators ranking as well as quality-value-based  ...  Query Classification Typically, the queries issued by users consist of a few words.  ... 
doi:10.7763/ijmlc.2014.v4.429 fatcat:hwo74szi3basbncn6lychcw2r4

Web personalization based on static information and dynamic user behavior

Massimiliano Albanese, Antonio Picariello, Carlo Sansone, Lucio Sansone
2004 Proceedings of the 6th annual ACM international workshop on Web information and data management - WIDM '04  
This strategy takes into account both static information, by means of classical clustering algorithms, and dynamic behavior of a user, proposing a novel and effective re-classification algorithm.  ...  In this paper, a web personalization strategy based on pattern recognition techniques is presented.  ...  Re-classification is used to overcome the inaccuracy of the registration information, based on the users' navigational behavior.  ... 
doi:10.1145/1031453.1031469 dblp:conf/widm/AlbanesePSS04 fatcat:c332liwzrvh2nksmwh4ktnskae

Special Issue on Device-Free Sensing for Human Behavior Recognition II (Addcorr from the editor)

Zhu Wang, Bin Guo, Yanyong Zhang, Daqing Zhang
2021 Personal and Ubiquitous Computing  
Meanwhile, an automatic classification technique is proposed to identify location types based on collected data.  ...  Based on the cluster characteristic, nearfield human presence can be recognized with online sensing.  ... 
doi:10.1007/s00779-020-01493-1 fatcat:leh6s4w7ynawpdgyj3uw6znyoy

Data-Driven Requirements Elicitation: A Systematic Literature Review

Sachiko Lim, Aron Henriksson, Jelena Zdravkovic
2021 SN Computer Science  
This article highlights the need for developing methods to leverage process-mediated and machine-generated data for requirements elicitation and addressing the issues related to variety, velocity, and  ...  for data processing.  ...  For clustering, topic modeling (16%) was the most commonly used approach, followed by more traditional clustering techniques (13%) and unsupervised rule-based clustering (2%).  ... 
doi:10.1007/s42979-020-00416-4 fatcat:g4g7nb4mwbhuhgmmfxi5vir5ly

Design of an Ensemble Learning Behavior Anomaly Detection Framework

Abdoulaye Diop, Nahid Emad, Thierry Winter, Mohamed Hilia
2019 Zenodo  
This model combines machine learning classification techniques and graph-based methods, relying on linear algebra and parallel computing techniques.  ...  In this paper, we present the step toward the construction of a user and entity behavior analysis framework by proposing a behavior anomaly detection model.  ...  These methods are based on clustering and outliers detection. The goal is to group users who showcase the same practices as one entity (i.e. clusters).  ... 
doi:10.5281/zenodo.3566298 fatcat:man7qwdaynhsdnfc2rvzqrdkbi

Effective personalized recommendation based on time-framed navigation clustering and association mining

Feng-Hsu Wang, Hsiu-Mei Shao
2004 Expert systems with applications  
Historical navigation sessions for each user are divided into frames of sessions based on a specific time interval.  ...  This research proposes a new clustering method, called HBM (Hierarchical Bisecting Medoids Algorithm) to cluster users based on the time-framed navigation sessions.  ...  On the other hand, to investigate the issue of whether it is better to consider the previous sessions ðS 1 ; S 2 ; …; S n21 Þ when doing user classification, another avenue of user classification based  ... 
doi:10.1016/j.eswa.2004.05.005 fatcat:km4s6ncf3vda7lruduw7wqxymy

SOME OBSERVATION OF ALGORITHMS DEVELOPED FOR ANOMALY DETECTION

Pallavi Raj, Rakhi Garg
2020 Indian Journal of Computer Science and Engineering  
The generated data are publicly available which may become the prime target for the malicious users, who try to attack and harm the innocent users.  ...  This paper mainly focuses on the graph mining techniques used for anomaly detection in social networks.  ...  At the final step, users and messages are co-clustered based on co-clustering algorithm non negative matrix trifactorization.  ... 
doi:10.21817/indjcse/2020/v11i1/201101005 fatcat:ynz45figozbu7fuznqo2u3j55y

Building MultiView Analyst Profile From Multidimensional Query Logs: From Consensual to Conflicting Preferences [article]

Eya Ben Ahmed, Ahlem Nabli, Faïez Gargouri
2012 arXiv   pre-print
In fact, the analyst preferences are clustered into three main pools : (i) consensual or non conflicting preferences referring to same preferences for all analysts; (ii) semi-conflicting preferences corresponding  ...  In this paper, we introduce a new approach for user profile construction from OLAP query logs. The key idea is to learn the user's preferences by drawing the evidence from OLAP logs.  ...  Via Clustering which is a simple metaclassifier that uses a cluster for classification.  ... 
arXiv:1203.3589v1 fatcat:wtbbqywoonfy5hm4n42vvexjli

Spiteful, One-Off, and Kind: Predicting Customer Feedback Behavior on Twitter [chapter]

Agus Sulistya, Abhishek Sharma, David Lo
2016 Lecture Notes in Computer Science  
Identifying different types of customers based on their feedback behavior can help companies to maintain their customers.  ...  In this paper, we use a machine learning approach to predict a customer's feedback behavior based on her first feedback tweet.  ...  Next, based on this representation, we cluster the users together. To cluster the users, we use Expectation-Maximization (E-M) algorithm.  ... 
doi:10.1007/978-3-319-47874-6_26 fatcat:jtlz7xvrivfv7hzotl3wshwkwm
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