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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
The Web represents one of the largest repositories of information ever compiled by mankind and as such search techniques are essential to navigating its depths and returning pertinent information. Typically the search techniques employed in search engines such as Google entail the use of keywords in which Web pages
paper also provides a search feature to searching the highest similarity of historical information, using text-mining and clustering methods. This makes it easier for users to learning historical event. We compare result of our idea into several device and several keyword to searching history. The experimental result show
Sentiment analysis in text mining is known to be a challenging task. Sentiment is subtly reflected by the tone, affective state or emotion of a writer's expression in words. Conventional text mining techniques which are based on keyword frequency counting usually run short of accurately detecting such subjective
This paper aims to design a system model that analyzes the unstructured data inside the posts about electronic products on social networking websites. For the purposes of this study, posts on social networking websites have been mined and the keywords are extracted from such posts. The extracted keywords and the
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