Web Intelligence

The explosive growth of resources available through the Internet, especially the emergence of social media, has created highly interactive platforms for users to create, share, exchange information and build social networks. This special issue includes several research outcomes which aim to develop technologies to help people exploit the information online in order to satisfy their information needs.

Personal learning is a hot research topic in the field of web-based learning systems as there is no one appropriate learning path for all students. Many researchers are using semantic web technologies to find new approaches to develop personalized learning environments based on describing knowledge using ontologies. In this paper, we investigate the personalization of learning process taking advantage of the Social Semantic Web, Learning Styles and Bayesian Networks to provide students with recommendations of collaborators and relevant resources that best fit their needs. The proposed personalization approach is based on discovering students’ learning styles by means of an analysing of their behaviours. Semantic Web concepts are applied to describe pertinent entities and variables of the proposed Bayesian Network, defined by use of inference mechanisms. Some experiments were conducted and results on students’ learning styles estimation are compared with those obtained by use of MBTI questionnaire.

In this work, we present a domain specific Information Retrieval (IR) system that identifies query and document topics and use them for better documents retrieval. We focus on retrieving documents having the specific types of information as that of the user query related to the tourism domain. Based on our past experience in handling tourism specific information, we observed that the query intent in the tourism domain largely span over a few major types. Based on this observation, we present an approach for document retrieval based on query and documents type identification. To do this, we have identified the major types (topics) in the tourism domain and built an ontology of the tourism domain. We developed a document classifier to identify the topic of web documents, and a query classifier to identify the topic of the user query, both pertaining to the tourism domain. The proposed IR system performs document retrieval by matching the type of user query with the matching type of documents. The experimental results show that the tourism specific topic identification of queries and documents improves the retrieval of documents having more specific information to satisfy user queries in the tourism domain.

With the extensive use of rating systems in the web, and their significance in decision making process by users, the need for more accurate aggregation methods has emerged. The Naïve aggregation method, using the simple mean, is not adequate anymore in providing accurate reputation scores for items [6], hence, several researches where conducted in order to provide more accurate alternative aggregation methods. Most of the current reputation models do not consider the distribution of ratings across the different possible ratings values. In this paper, we propose a novel reputation model, which generates more accurate reputation scores for items by deploying the normal distribution over ratings. Experiments show promising results for our proposed model over state-of-the-art ones on sparse and dense datasets.

With the rapid proliferation of online-blogging and micro-blogging websites, millions of text posts are generated and made available online every day. Utilizing this rich data channel could facilitate educated purchasing of items, discovering trends and public tendencies regarding various products available in the market, discovering political inclination of societies prior to a national election, etc. Since the last decade, Sentiment Analysis (SA) has received increased attention from many researchers as a method for addressing topics, such as the aforementioned ones. This paper focuses on SA using sentiment features and patterns. We propose different sentiment polarity detection methods, two unsupervised methods and one supervised, which we compare with two baseline methods, a state-of-the-art Support Vector Machine (SVM) classifier trained on a unigram bag-of-words model, and an unsupervised SentiStrength [38] algorithm. In our experiments, we show that our polarity detection methods are highly effective and can outperform the aforementioned baselines in most of our conducted experiments.

The percentage of older adults using social media has increased substantially in recent years, yet little research has been done to understand the foundations underlying social media technology usage by older adults. Such an understanding is useful for developing intelligent user modeling and personalization techniques specific to this growing community. The current work first compared characteristics of Facebook users to non-users among adults age 51 to 91 and found that older adult Facebook users were significantly more satisfied with their current social roles than non-users. Second, we explored several characteristics of active older adult Facebook users, providing detailed data regarding the ways in which they access social media, the kinds of personal information they typically share, and information about their public versus private communication practices, preferences, and concerns. Finally, we examined specific relationships between older adults’ Facebook communication habits and their attitudes regarding perceived loneliness and social role satisfaction. Controlling for factors such as age, gender, ethnicity, socioeconomic status (education and income), and marital status, we found that directed communications (as opposed to broadcast communications or passive consumption of content) was correlated with reduced loneliness as well as increased social role satisfaction among this distinct population.