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Identifying prospective customers is an important aspect of marketing research. In this paper, we provide support for a new type of query, the Reverse Top-k Geo-SocialKeyword (RkGSK) query. This query takes into account spatial, textual, and social information, and finds prospective customers for geotagged objects
boundaries. In order to investigate the evolutionary trends of keyword-sharing groups in academic publications, we applied three types of Social Network Analysis methods on the papers in the Computer Science domain collected from the Libra database. Particularly, papers published in 5 different time periods from 1970 to 2010
gSocial computingh is a keyword in contemporary society. However, if we ask anew what the term social computing means, we realize that its definition, meaning and modeling have not necessarily been clarified. This paper first investigates when questions are raised about gsocial computing.h We find that the oldest
The continued exponential growth in volume of literature data is giving birth to a new challenge to the bibliographic analysis service and the traditional features such as keyword search, author search and statistics services could not satisfy researchers for in-depth analysis. The emerging of community analysis in
query, in terms of keyword(s) to describe the query topic, while using only the citation graph and the keywords associated with the articles, and no latent information. We use a novel keyword expansion step, inspired by community finding in social network analysis, in DiSCern to ensure that the semantically correlated
system, classification of keywords by higher ranking of topics has contributed to an active role for the extraction of summarization, the results of summarization ratio in social web is 40%-50%.
Efficient organization and analysis of academic information has many advantages. Most scholar retrieval systems appeared these years can perform keyword-based paper search. However, performing large-scale expert and paper retrieval is an intractable problem. Here we present a platform that can not only reduce the
accompanied by dedicated views to explore the semantic similarities between scientific articles. This paper focuses on applying our approach on a dataset of 519 project proposal abstracts, with the intention to bring value to the current indexation methodologies that rely primarily on co-citations and keyword matching. Our
Despite science's great intellectual prestige, developing robot scientists will probably be simpler than developing general AI systems because there is no essential need to take into account the social milieu.
LITMUS is a real-time online and openly accessible service that collects high quality information on landslide events from social media. This service uses disaster related keywords, such as "landslide" and "mudslide", to analyze messages posted by English speaking users. However
This article describes an algorithm to facilitate the proper assignment of reviewers by finding an author's profile. It uses an original approach to analyzing publications published in digital libraries to get additional keywords based on NLP (natural language processing) techniques. Comparing profiles and finding
The main objective of this paper is to compare the sentiments that prevailed before and after the presidential elections, held in both US and France in the year 2012. To achieve this objective we extracted the content information from a social medium such as Twitter and used the tweets from electoral candidates and
This study introduces a new soft computing method for expert identification in social networks based on formal concept analysis and fuzzy rules. Expert identification is an important task in social network analysis and there are several methods to identify people who have experience in given area. In this paper, we
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