01872nas a2200205   4500008004100000245007300041210006900114260000900183300001400192490000600206520120000212653002101412653001801433653002301451653002301474653001601497100001801513700001801531856011701549       2011                        eng d00aDetecting human sentiment from text using a proximity-based approach0 aDetecting human sentiment from text using a proximitybased appro  c2011  a206 - 2120 v93 aSentiment analysis seeks to characterize opinionated or evaluative aspects of natural language text thus helping people to discover valuable information from large amounts of unstructured data. Sentiment analysis can be used for grouping search engine results, analyzing news content, reviews for books, movie, sports, blogs, web forums, etc. Several methods have been proposed for sentiment analysis at word, sentence, and document levels mostly based on common machine learning techniques. In this paper we explore a new methodology for sentiment analysis by considering a new set of features based on word proximities in a written text. We propose three proximity-based features, namely, proximity distribution, mutual information between proximity types, and proximity patterns. We applied this approach to the analysis of movie reviews, drug reviews, and music reviews. Our experimental results show that proximity-based sentiment analysis is able to extract sentiments from a specific domain, with performance comparable to the state-of-the-art. To the best of our knowledge, this is the first attempt at focusing on only proximity-based features as the primary cues in sentiment analysis.10aMachine learning10aMovie reviews10aProximity analysis10aSentiment analysis10aText mining1 aHasan, S.M.S.1 aAdjeroh, D.A.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84855397249&partnerID=40&md5=a009c46b7b54e127e88ca882940b976702181nas 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=29f4612a2a8b6038c4d5f5e37fcbd67b02444nas a2200217   4500008004100000245007500041210006900116260000900185300001400194490000600208520172600214653002901940653002801969653002201997653002702019653001602046100001602062700001502078700001602093856011702109       2008                        eng d00aHybrid clustering approach for term partitioning in document data sets0 aHybrid clustering approach for term partitioning in document dat  c2008  a272 - 2770 v63 aInformation retrieval is one of the major research areas due to accumulation of huge information in digital form. Various techniques of Information retrieval are based on the fact that terms contained in a document along with their frequency of occurrence signify the semantics of the document. Recent attempts to find the relevant document for a context represents documents in a vector space model as document-term vector containing term weights for every index term in that document. As there will be enormous number of index terms this leads to high dimensionality problem. We can reduce the dimensionality based on the observation that groups of terms associated with related concepts occur together or do not occur in a document based on whether the document is relevant or not to that concept. Such a group of terms Is identified as an equivalence class and can be viewed as a single dimension in a Rough set based information retrieval system. In this paper we present a hybrid clustering approach for the formation of equivalence classes of terms associated with related concepts. It uses the outcome of hierarchical clustering to provide seed points for implementing Incremental K-means algorithm. Due to the sparsity of the term vector the cosine similarity estimate was found to be ineffective for term clustering. Another promising measure of proximity estimate used in information retrieval namely Euclidian distance has a drawback that It is biased towards changes in the term frequencies in larger documents when the term weights are represented by tf-ldf estimates. Hence we propose normalization for tf-idf estimates while representing a term as a vector in a document space before clustering the terms.10aDimensionality reduction10aHierarchical clustering10aK means algorithm10aPartitional clustering10aText mining1 aReddy, K.T.1 aShashi, M.1 aReddy, L.P.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-70350735762&partnerID=40&md5=911fe260f9a7d869bf7ee8660851cdac01253nas a2200181   4500008004100000245004200041210004200083260000900125300001400134490000600148520070600154653002100860653001600881653002000897653001800917100001900935856011700954       2006                        eng d00aFrom words to concepts in text mining0 aFrom words to concepts in text mining  c2006  a147 - 1490 v43 aIn the text mining process the interface between the natural language text and content reflecting terms is essential for the successful indexing process. The traditional interfaces such as the sauri and lexicons have limitations for web content processing. The recent lexical nets have promises; but their efficiency needs to be tested. The current work measures the use of word relations specified by lexical nets in a large test bed. The results correlate with similar recent past studies as not all specified word relations in the lexical nets are semantically rich in expressing the conceptual relations between given words. The results call for applying more heuristic approaches for text mining.10aConcept indexing10aText mining10aText processing10aWord relation1 aAl-Zahrani, S.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-44649133985&partnerID=40&md5=46dbcc4ccd511a6a729ef7fac8d7713f