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This paper presents a keyword extraction technique that can be used for tracking topics over time. In our work, keywords are a set of significant words in an article that gives high-level description of its contents to readers. Identifying keywords from a large amount of on-line news data is very useful in that it can
provide simple message analysis features such as browsing and simple keyword-based searching of the recorded messages. In this paper, we propose a system, called IMAnalysis, that supports intelligent chat message analysis using text mining techniques. The IMAnalysis system provides functions on chat message retrieval, social
order, without any content based grouping. This paper presents an experimental deduction of a search result clustering methodology to group the links, returned by the search result page for a particular keyword, based on the contents of the HTML documents, represented by the links and label these resulted groups
Text mining is the technique that helps users find useful information from a large amount of digital text documents on the Web or databases. Instead of the keyword-based approach which is typically used in this field, the pattern-based model containing frequent sequential patterns is employed to perform the same
important to provide users with valuable information about goods of any category. The objective of this research is to improve the usefulness of reviews for consumers. This research focuses on an opinion dictionary as a collection of specific keywords and key phrases. This opinion dictionary models a standardized better review
specific "stopwords" allow greater efficiency in the extraction of terms and keywords from the subjects addressed in the texts analyzed. This result may be applicable in a fraud audit scenario that involves selecting a significant number of documents for reading with previously unknown content.
value, assigning method should not constructed only keywords matching. It need to identify themes of text and extract part of text for that the theme corresponds to query.
documents based on keywords, users normally have a more abstract perception what information they require. Semantic gap, which is the disparity between user's request and query results, has been identified as a challenging issue. In this paper, we are interested in scientific document indexing for retrieval. Knowing the
keywords of different languages are also revealed. We conducted experiments on a set of Chinese-English bilingual parallel corpora to discover the relationships between documents of these languages.
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