Analisis Pemodelan User Untuk Social Semantic Web Dengan Pendekatan SWUM (Socail Web User Model)

ABSTRAKSI: Dari tahun ke tahun, aplikasi dan pengguna Social Web semakin banyak di World Wide Web (www). Antar pengguna saling bertukar informasi pada setiap apliksi Social Web yang berbeda-beda. Semantic Web merupakan suatu cara untuk menyatukan interaktifitas pengguna, kolaborasi informasi, mendukung agregasi data dan berbagi data.Penelitian ini melakukan analisis terhadap kebutuhan dari model pengguna yang mendukung proses berbagi data dan agregasi data pengguna untuk meningkatkan layanan personalisasi dan rekomendasi.Tiga aplikasi yang dipilih menjadi data set yaitu Facebook, Twitter dan Google plus. Ketiga aplikasi ini dianalisis untuk mengetahui atribut dari model pengguna umum yang memungkinkan berbagi data pengguna dan menganalisis apa yang dibutuhkan untuk meningkatkan model pengguna dengan melakukan agregasi pada masing-masing atribut. Sehingga, penelitian ini menggunakan pendekatan Social Web User Model (SWUM) yang merupakan pemodelan baru untuk memenuhi kebutuhan pada aplikasi Social Web tersebut. Terdapat tiga tahapan utama untuk pembangunan model dengan SWUM yaitu proses pemilihan data set, proses requirement for SWUM dan profile aggregation with the SWUM. Selain itu, didalam proses requirement for SWUM terdapat tiga sub proses di dalamnya yaitu User Model Dimensions, User Model Attributes dan User Model WordNet yang dipakai untuk mencari profile aggregation dan mengatasi masalah keberagaman atribut sebagai bagian dari model pengguna untuk mendukung agregasi pengguna model.Hasil analisis penelitian ini berupa tabel agregasi atribut pada setiap aplikasi data set Facebook, Twitter dan Google plus. Terdapat beberapa rekomendasi atribut untuk menyeragamkan atribut yang ada di ketiga aplikasi data set.Kata Kunci : SWUM, agregasi, berbagi data, model penggunaABSTRACT: From year to year, more applications and users of Social Web on the World Wide Web (www). Between users are exchange the information in every different Social Web. Semantic Web is a way to unify the user interactivity, information collaboration, support data aggregation and data sharing.This research conducted an analysis the needs of the user model that supports the process of data sharing and aggregation of user data to improve personalization and recommendation services.Three applications are chosen to be a data set including Facebook, Twitter and Google plus. They are analyzed to determine the attributes of the general user model which allows sharing of user data and analyze what is needed to improve the user model with doing aggregation on each attribute. Thus, this research uses the Social Web User Model (SWUM) approach which is a new model to satisfy the needs of the Social Web application. There are three main stages to develop model with SWUM including the process of selecting a data set, the requirement for SWUM and profile aggregation with the SWUM. In addition, in the requirement for SWUM, there are three sub processes within such as the User Model Dimensions, User Model Attributes and User Model WordNet that used to find profile aggregation and slove the diversity of attribues issues as part of the user model to support the aggregation of the user model.The results of this research are tables of aggregation attributes on each application data set that Facebook, Twitter and Google plus. There are some recommendations attributes for uniformity of attributes in the third applications of data set.Keyword: SWUM, aggregation, data sharing, user mode

Analysing and Visualizing Tweets for U.S. President Popularity

In our society we are continually invested by a stream of information (opinions, preferences, comments, etc.). This shows how Twitter users react to news or events that they attend or take part in real time and with interest. In this context it becomes essential to have the appropriate tools in order to be able to analyze and extract data and information hidden in their large number of tweets. Social networks are a source of information with no rivals in terms of amount and variety of information that can be extracted from them. We propose an approach to analyze, with the help of automated tools, comments and opinions taken from social media in a real time environment. We developed a software system in R based on the Bayesian approach for text categorization. We aim of identifying sentiments expressed by the tweets posted on the Twitter social platform. The analysis of sentiment spread on social networks allows to identify the free thoughts, expressed authentically. In particular, we analyze the sentiments related to U.S President popularity by also visualizing tweets on a map. This allows to make an additional analysis of the real time reactions of people by associating the reaction of the single person who posted the tweet to his real time position in Unites States. In particular, we provide a visualization based on the geographical analysis of the sentiments of the users who posted the tweets

Analysing and Visualizing Tweets for U.S. President Popularity

In our society we are continually invested by a stream of information (opinions, preferences, comments, etc.). This shows how Twitter users react to news or events that they attend or take part in real time and with interest. In this context it becomes essential to have the appropriate tools in order to be able to analyze and extract data and information hidden in their large number of tweets. Social networks are a source of information with no rivals in terms of amount and variety of information that can be extracted from them. We propose an approach to analyze, with the help of automated tools, comments and opinions taken from social media in a real time environment. We developed a software system in R based on the Bayesian approach for text categorization. We aim of identifying sentiments expressed by the tweets posted on the Twitter social platform. The analysis of sentiment spread on social networks allows to identify the free thoughts, expressed authentically. In particular, we analyze the sentiments related to U.S President popularity by also visualizing tweets on a map. This allows to make an additional analysis of the real time reactions of people by associating the reaction of the single person who posted the tweet to his real time position in Unites States. In particular, we provide a visualization based on the geographical analysis of the sentiments of the users who posted the tweets

Developing Marketing Personas with Machine Learning for Educational Program Finder

The motivation for the work is to see if marketing personas can be created with an educational Program Finder using machine learning. The research questions for the master’s thesis are “By using machine learning to process user behaviour, will the marketing personas improve in quality?” and “Can marketing and sales benefit from machine learning made personas?”. With the first research question, the thesis uses existing marketing personas created by Aalto University Executive Education and references them with the marketing personas created with machine learning. The second research question is answered by conducting three end-user interviews. The end-users all had marketing and sales working context and were chosen from Aalto University Executive Education. The approach for the thesis is to create a hypothesis of machine learning algorithms that could create marketing personas. The machine learning framework chosen for the thesis is semi-structured that implements labelled clusters to which build the user behaviour to. User behaviour is collected from users interacting with the filters of an educational Program Finder. The thesis introduces a marketing persona, Generic Marketing Persona and for a deeper analysis, the Data Behind the Persona. The Generic Marketing Persona uses the machine learning algorithms and is created from the labelled clusters. The Generic Marketing Persona has a template for which to build on and uses the cluster data to enrich the template with the data. The Data Behind the Persona is a presentation of charts that are extracted from the cluster data. The results for the thesis are that the machine learning personas increased the quality when referenced to the existing ones. The machine learning personas were more detailed, based on data and communicated the needs of the target groups more efficiently. However, the Generic Marketing Persona was proven to be unusable for taking marketing and sales actions because the information was too generic. Interviewees though found many possible use cases for the Data Behind the Persona, including content producing, target group revision, lead valuing and market trend analysis

Developing Marketing Personas with Machine Learning for Educational Program Finder

The motivation for the work is to see if marketing personas can be created with an educational Program Finder using machine learning. The research questions for the master’s thesis are “By using machine learning to process user behaviour, will the marketing personas improve in quality?” and “Can marketing and sales benefit from machine learning made personas?”. With the first research question, the thesis uses existing marketing personas created by Aalto University Executive Education and references them with the marketing personas created with machine learning. The second research question is answered by conducting three end-user interviews. The end-users all had marketing and sales working context and were chosen from Aalto University Executive Education. The approach for the thesis is to create a hypothesis of machine learning algorithms that could create marketing personas. The machine learning framework chosen for the thesis is semi-structured that implements labelled clusters to which build the user behaviour to. User behaviour is collected from users interacting with the filters of an educational Program Finder. The thesis introduces a marketing persona, Generic Marketing Persona and for a deeper analysis, the Data Behind the Persona. The Generic Marketing Persona uses the machine learning algorithms and is created from the labelled clusters. The Generic Marketing Persona has a template for which to build on and uses the cluster data to enrich the template with the data. The Data Behind the Persona is a presentation of charts that are extracted from the cluster data. The results for the thesis are that the machine learning personas increased the quality when referenced to the existing ones. The machine learning personas were more detailed, based on data and communicated the needs of the target groups more efficiently. However, the Generic Marketing Persona was proven to be unusable for taking marketing and sales actions because the information was too generic. Interviewees though found many possible use cases for the Data Behind the Persona, including content producing, target group revision, lead valuing and market trend analysis

Semantic Approach to Model Diversity in a Social Cloud

Understanding diversity is important in our inclusive society to hedge against ignorance and accommodate plural perspectives. Diversity nowadays can be observed in online social spaces. People from different backgrounds (e.g. gender, age, culture, expertise) are interacting every day around online digital objects (e.g. videos, images and web articles) leaving their social content in different format, commonly as textual comments and profiles. The social clouds around digital objects (i.e. user comments, user profiles and other metadata of digital objects) offer rich source of information about the users and their perspectives on different domains. Although, researchers from disparate disciplines have been working on understanding and measuring diversity from different perspectives, little has been done to automatically measure diversity in social clouds. This is the main objective of this research. This research proposes a semantic driven computational model to systematically represent and automatically measure diversity in a social cloud. Definitions from a prominent diversity framework and Semantic Web techniques underpin the proposed model. Diversity is measured based on four diversity indices - variety, balance, coverage and (within and across) disparity with regards to two perspectives – (a) domain, which is captured in user comments and represented by domain ontologies, and (b) user, which is captured in profiles of users who made the comments and represented by a proposed User Diversity Ontology. The proposed model is operationalised resulting in a Semantic Driven Diversity Analytics Tool (SeDDAT), which is responsible for diversity profiling based on the diversity indices. The proposed approach of applying the model is illustrated on social clouds from two social spaces - open (YouTube) and closed (Active Video Watching (AVW-Space)). The open social cloud shows the applicability of the model to generate diversity profiles of a large pool of videos (600) with thousands of users and comments. Closed social clouds of two user groups around same set of videos illustrate transferability and further utility of the model. A list of possible diversity patterns within social clouds is provided, which in turn deepen the understanding of diversity and open doors for further utilities of the diversity profiles. The proposed model is applicable in similar scenarios, such as in the social clouds around MOOCs and news articles

Tematski zbornik radova međunarodnog značaja. Tom 3 / Međunarodni naučni skup “Dani Arčibalda Rajsa”, Beograd, 3-4. mart 2015.

In front of you is the Thematic Collection of Papers presented at the International Scientific Confer-ence “Archibald Reiss Days”, which was organized by the Academy of Criminalistic and Police Studies in Belgrade, in co-operation with the Ministry of Interior and the Ministry of Education, Science and Techno-logical Development of the Republic of Serbia, National Police University of China, Lviv State University of Internal Affairs, Volgograd Academy of the Russian Internal Affairs Ministry, Faculty of Security in Skopje, Faculty of Criminal Justice and Security in Ljubljana, Police Academy “Alexandru Ioan Cuza“ in Bucharest, Academy of Police Force in Bratislava and Police College in Banjaluka, and held at the Academy of Crimi-nalistic and Police Studies, on 3 and 4 March 2015.International Scientific Conference “Archibald Reiss Days” is organized for the fifth time in a row, in memory of the founder and director of the first modern higher police school in Serbia, Rodolphe Archibald Reiss, PhD, after whom the Conference was named.The Thematic Collection of Papers contains 168 papers written by eminent scholars in the field of law, security, criminalistics, police studies, forensics, informatics, as well as members of national security system participating in education of the police, army and other security services from Spain, Russia, Ukraine, Bela-rus, China, Poland, Armenia, Portugal, Turkey, Austria, Slovakia, Hungary, Slovenia, Macedonia, Croatia, Montenegro, Bosnia and Herzegovina, Republic of Srpska and Serbia. Each paper has been reviewed by two reviewers, international experts competent for the field to which the paper is related, and the Thematic Conference Proceedings in whole has been reviewed by five competent international reviewers.The papers published in the Thematic Collection of Papers contain the overview of contemporary trends in the development of police education system, development of the police and contemporary secu-rity, criminalistic and forensic concepts. Furthermore, they provide us with the analysis of the rule of law activities in crime suppression, situation and trends in the above-mentioned fields, as well as suggestions on how to systematically deal with these issues. The Collection of Papers represents a significant contribution to the existing fund of scientific and expert knowledge in the field of criminalistic, security, penal and legal theory and practice. Publication of this Collection contributes to improving of mutual cooperation between educational, scientific and expert institutions at national, regional and international level