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Social review sites offer a wealth of information beyond sentiment polarity. For instance, on IMDb users leave valuable reviews on different aspects of a movie (e.g. Actors, visual effects). This inspires researchers to fully discover information from social review texts for the sake of modelling users behavior
main issue is related to the management and classification of information provided by users. This paper introduces a folksonomy approach, the architecture of the system, and the results of an experiment about keyword recommendations.
investigation about the improvements in the accuracy of a search system provided by network analysis techniques supporting the discovery of relations among the items stored in the repository. For this reason, we have developed the SEEN prototype, a keyword search tool exploiting network analysis. SEEN has been evaluated against a
This study proposes an empirical way for determining probability of network tie formation between network actors. In social network analysis, it is a usually problem that information for determining whether or not a network tie should be formed is missing for some network actors, and thus network can only be partially
Recently, with the increasing of users and activities in social network service, an image sentiment analysis has been an important keyword for psychological study and commercial marketing. To recognize accurately user's sentiments of the image, it is essential to identify discriminative visual features and then
In a social tagging system, resources (such as photos, video and web pages) are associated with tags. These tags allow the resources to be effectively searched through tag-based keyword matching using traditional IR techniques. We note that in many such systems, tags of a resource are often assigned by a diverse
author suggested keywords in scientific papers of Faculty of Electrical Engineering and Computing (FER), IEEE terms and user tags in academic social network Mendeley. This paper will examine the scientific papers represented in IEEE Journals and Magazines indexed in the IEEE/IET Electronic Library reviewing the overlap
In this paper, we propose a new method of classifying tendencies and opinions in texts of multiple sentence length extracted from social media and covering both formal and informal vocabularies. To extract contextual information from the texts, we carry out computations based on keywords, the position of the sentence
Question Understanding of Chinese Question-Answering System generally includes steps such as: word segmentation, POS Tagging, keywords expansion, information retrieval etc. The extended keyword set usually has redundant messages and part of the words and phrases may be not relevant to the question. Consequently
priori capture the composition of a social group of interest using static keywords. Capturing such dynamic compositions is crucial to both understanding the true membership of social groups and in providing high-quality data for downstream applications such as trend forecasting. We propose a novel unsupervised learning
Social tagging is a relatively new type of social software that stores user-generated textual keywords to describe a resource or aspects of that resource. In this paper we explore the mechanisms that social tagging can trigger to change the behavior of knowledge workers. We argue that social tagging has the potential
Given the plethora of social networking sites, it can be difficult for users to browse too many sites and discover social friends. For example, for a new diabetes patient, how can s/he find the users with similar symptoms on different dedicated sites and form supporting groups with them? Since different sites may use
A social network is a network of interactions between entities of social interest like people, organisations, hobbies and transactions. Finding relevant associations between entities in a social network is of great value in many areas like friendship networks, biology and countering terrorism. Semantic Web technology
Social bookmarking tools are rapidly emerging on the Web as it can be witnessed by the overwhelming number of participants. In such spaces, users annotate resources by means of any keyword or tag that they find relevant, giving raise to lightweight conceptual structures aka folksonomies. In this respect, needless to
Social media such as Twitter, Google+, Facebook, etc has an undeniable effect on the way information is stored and processed by us. The information available on the web is abound and hence it is essential to mine the important information and avoid the irrelevant details. Along with this, it is beneficial to
Web page recommendation model traces userspsila Web-surfing trails, extracts the useful information including keywords, Web page URLs and userspsila evaluations on Web pages, and automatically generates FCA (formal concept analysis) knowledge base and enterprise ontology knowledge base with WordNet. While users are
In this paper we present a framework that extracts meaningful knowledge from microposts shared in social platforms in order to build user profiles. This process involves different steps for the analysis of such microposts (extraction of keywords, named entities and their matching to ontological concepts) and their
questionnaire based survey was conducted using 40 Cypriot citizens divided into two age groups who were asked to annotate an image dataset using a vocabulary of 52 keywords. Our results indicate that there are age differences in the way people annotate images, while the gender differences are smaller than our assumptions
algorithms, web image information is extracted from textual sources such as image file names, anchor texts, existing keywords and, of course, surrounding text. However, the systems that attempt to mine information for images using surrounding text suffer from several problems, such as the inability to correctly assign all
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