02181nas a2200205   4500008004100000245008400041210006900125260000900194300001400203490000600217520150200223653002001725653001901745653002301764653001601787100001501803700002201818700001801840856011701858       2009                        eng d00aDeriving the impact of scientific publications by mining citation opinion terms0 aDeriving the impact of scientific publications by mining citatio  c2009  a282 - 2880 v73 aOne of the most important measures for estimating the impact of scientific publications is the number of citations they have received. To day, there exist several tools and metrics to evaluate the relative importance of individual papers and publication venues based on their citation distribution. Despite their acknowledged usefulness, most of the existing techniques rely on quantitative rather than qualitative aspects of the citation analysis and thus they are inherently limited in conveying any specific information about the author opinions to wards the papers they cite. In this paper, we introduce a method that combines text mining and lexical analysis in order to elucidate the authors' attitude towards the works they cite in their publications. We have applied our method on a set of 4,520 citations that span to 40 publications and tried to shed some light on the following issues. How often authors express an opinion about the papers they cite? Do all authors who cite the same publication share a common understanding on that publication's impact? Does the citations' context influence people's perception of the referred papers and how we can take context into consideration? Can we define a qualitative measure for esti mating the impact of scientific publications? Our evaluation shows that although authors do not always express their personal opinions about the papers they cite, their judgments (when articulated) have a great influence on the papers' perceived importance.10aCitation impact10aOpinion mining10aSentiment analysis10aText mining1 aStamou, S.1 aMpouloumpasis, N.1 aKozanidis, L.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-77953259909&partnerID=40&md5=29f4612a2a8b6038c4d5f5e37fcbd67b02056nas a2200229   4500008004100000245006800041210006600109260000900175300001400184490000600198520133500204653002001539653002201559653001801581653002701599653002101626100001501647700001801662700001501680700001401695856011701709       2008                        eng d00aA recommendation model based on site semantics and usage mining0 arecommendation model based on site semantics and usage mining  c2008  a244 - 2550 v63 aThe explosive growth of online data and the diversity of goals that may be pursued over the web have significantly increased the monetary value of the web traffic. To tap into this accelerating market, web site operators try to increase their traffic by customizing their sites to the needs of specific users. Web site customization Involves three great challenges: (I) the accurate identification of the user Interests in the sites' content (II) the detection of the user goals in their site visits and (III) the encapsulation of the user interests and goals into the sites' presentation and content. In this paper, we study how we can effectively identify the user interests and goals in their site visits and we evaluate their correlation as this is exemplified in the users' navigational patterns and the site's semantic content and structure. Based on our findings we propose a novel recommendation mechanism that employs web mining techniques for suggesting customized site views that satisfy both the user preferences and goals. Our experimental evaluation shows that the user site interests and interaction goals can be accurately detected from the users' navigational behavior and that our recommendation model, which uses the identified user preferences and goals yields significant improvements in the sites' usability.10aRecommendations10aSubject hierarchy10aUser profiles10aWeb site customization10aWeb usage mining1 aStamou, S.1 aKozanidis, L.1 aTzekou, P.1 aZotos, N.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-70350741026&partnerID=40&md5=9afb2fa2b33a9de355f329041b572518