Matches in ScholarlyData for { ?s ?p ?o. }
- 15 title "Detecting Collective Attention Spam".
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- item-1 hasContent kyumin-lee.
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- item-2 hasContent james-caverlee.
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- item-3 hasContent krishna-kamath.
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- item-4 hasContent zhiyuan-cheng.
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- 17 creator luz-rello.
- 17 creator ricardo-baeza-yates.
- 17 type InProceedings.
- 17 label "On Measuring the Lexical Quality of the Web".
- 17 sameAs 17.
- 17 abstract "In this paper we propose a measure for estimating the lexical quality of the Web, that is, the representational aspect of the textual web content. Our lexical quality measure is based in a small corpus of spelling errors and we apply it to English and Spanish. We first compute the correlation of our measure with web popularity measures to show that gives independent information and then we apply it to different web segments, including social media. Our results shed a light on the lexical quality of the Web and show that authoritative websites have several orders of magnitude less misspellings than the overall Web. We also present an analysis of geographical distribution of lexical quality throughout English and Spanish speaking countries.".
- 17 hasAuthorList authorList.
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- 17 keyword "Web text accessibility".
- 17 keyword "lexical quality".
- 17 keyword "social media".
- 17 title "On Measuring the Lexical Quality of the Web".
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- item-1 hasContent ricardo-baeza-yates.
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- item-2 hasContent luz-rello.
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- 18 creator haijie-gu.
- 18 creator rishi-chandy.
- 18 type InProceedings.
- 18 label "Identifying Spam in the iOS App Store".
- 18 sameAs 18.
- 18 abstract "Popular apps on the Apple iOS App Store can generate millions of dollars in profit and collect valuable personal user information. Fraudulent reviews could deceive users into downloading potentially harmful spam apps or unfairly ignoring apps that are victims of review spam, thus automatically identifying spam in the App Store is an important problem. This paper aims to introduce and characterize novel datasets acquired through crawling the iOS App Store, compare a baseline Decision Tree model with a novel Latent Class graphical model for classification of app spam, and analyze preliminary results for clustering reviews.".
- 18 hasAuthorList authorList.
- 18 isPartOf proceedings.
- 18 keyword "mobile apps".
- 18 keyword "opinion spam".
- 18 keyword "review spam".
- 18 title "Identifying Spam in the iOS App Store".
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- item-1 hasContent rishi-chandy.
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- 19 creator aoying-zhou.
- 19 creator jingwei-zhang.
- 19 creator xiaoling-wang.
- 19 creator yuming-lin.
- 19 type InProceedings.
- 19 label "An Information Theoretic Approach to Sentimental Polarity Classification".
- 19 sameAs 19.
- 19 abstract "Sentiment classification is a task of classifying documents according to their overall sentimental inclination. It is very important and popular in many web applications, such as analysis of credibility of news sites on the web, recommendation system and mining online discussion. Vector space model is widely applied on modeling documents in supervised sentiment classification, in which the feature presentation (including features type and weighting method) is crucial for classification accuracy. The traditional feature presentation methods of text categorization do not perform well in sentiment classification, because the expressing manners of sentiment are more subtle. We analyze the relationships of terms with sentimental labels based on information theory, and propose applying information theoretic approach on sentiment classification of documents. In this paper, the sentimental polarities of the terms in a document are quantified by mutual information. And then the terms are weighted in vector space based on their sentiment scores and contribution to the document. We perform extensive experiments with SVM on the sets of multiple products reviews, and the experimental results show our approach is more effective than the traditional ones.".
- 19 hasAuthorList authorList.
- 19 isPartOf proceedings.
- 19 keyword "feature presentation".
- 19 keyword "information theory".
- 19 keyword "mutual information".
- 19 keyword "sentiment classification".
- 19 title "An Information Theoretic Approach to Sentimental Polarity Classification".
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- item-1 hasContent yuming-lin.
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