SUPERVISED TERM WEIGHTING METHODS FOR URL CLASSIFICATION
نویسندگان
چکیده
منابع مشابه
Supervised Term Weighting Methods for URL Classification
Many term weighting methods are suggested in the literature for Information Retrieval and Text Categorization. Term weighting method, a part of feature selection process is not yet explored for URL classification problem. We classify a web page using its URL alone without fetching its content and hence URL based classification is faster than other methods. In this study, we investigate the use ...
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Text Classification is one of the booming area in research with the availability of huge amount of electronic data in the form of news article, research articles, email message, blog, web pages etc. Text Representation is a vital step for text classification. In text representation, term weighting method assigns appropriate weights to the term to get better performance; the term weighting metho...
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Recently the research on supervised term weighting has attracted growing attention in the field of Traditional Text Categorization (TTC) and Sentiment Analysis (SA). Despite their impressive achievements, we show that existing methods more or less suffer from the problem of over-weighting. Overlooked by prior studies, over-weighting is a new concept proposed in this paper. To address this probl...
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We provide a simple but novel supervised weighting scheme for adjusting term frequency in tf-idf for sentiment analysis and text classification. We compare our method to baseline weighting schemes and find that it outperforms them on multiple benchmarks. The method is robust and works well on both snippets and longer documents.
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In text categorization, the class agnostic (unsupervised) tf× idf term weighting scheme has seen widespread usage. Recently proposed supervised term weighting methods including tf×rf and tf× δidf make use of term class distribution to improve the classification accuracy. However, they only account for the presence of terms in classes, ignoring the absence of key categorical terms, which may giv...
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ژورنال
عنوان ژورنال: Journal of Computer Science
سال: 2014
ISSN: 1549-3636
DOI: 10.3844/jcssp.2014.1969.1976