@article {290,
	title = {Detecting human sentiment from text using a proximity-based approach},
	journal = {Journal of Digital Information Management},
	volume = {9},
	year = {2011},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2011},
	pages = {206 - 212},
	abstract = {Sentiment 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.},
	keywords = {Machine learning, Movie reviews, Proximity analysis, Sentiment analysis, Text mining},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84855397249\&partnerID=40\&md5=a009c46b7b54e127e88ca882940b9767},
	author = {Hasan, S.M.S. and Adjeroh, D.A.}
}
@article {202,
	title = {Deriving the impact of scientific publications by mining citation opinion terms},
	journal = {Journal of Digital Information Management},
	volume = {7},
	year = {2009},
	note = {Export Date: 10 July 2014},
	month = {2009},
	pages = {282 - 288},
	abstract = {One 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{\textquoteright} 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{\textquoteright}s impact? Does the citations{\textquoteright} context influence people{\textquoteright}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{\textquoteright} perceived importance.},
	keywords = {Citation impact, Opinion mining, Sentiment analysis, Text mining},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77953259909\&partnerID=40\&md5=29f4612a2a8b6038c4d5f5e37fcbd67b},
	author = {Stamou, S. and Mpouloumpasis, N. and Kozanidis, L.}
}
