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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 demand for extracting keywords related to national issues from various sources and using them to retrieve R&D information has increased rapidly recently. In order to satisfy this demand, three methodologies are proposed in this study: a hybrid methodology for extracting and integrating national issue
of that unrest on Phuket's tourism environment. It is proposed that this analysis can provide measurable insights through summarization, keyword analysis and clustering. We measure sentiment using a binary choice keyword algorithm. A multi-knowledge based approach is proposed using, Self-Organizing Maps along with
Indonesians may not tolerated swear words. Some Indonesian swear words may have multiple means, not always an Indonesian swear word means insulting. Twitter has provide tweet's data by account, trending topics, and advance keyword. This work try to analyze many tweet about political news, political event, and some Indonesian
. This process consists of three phases: data cleaning, data extraction, and data consolidation. Data cleaning is done by validating the format for each email. Data extraction consists of keyword extraction, sentiment analysis, regular expression, entity extraction and summary extraction. Data consolidation is used to
This paper reports on the work on a new service using text mining on SMS data: SMSTrends. The service extracts trends in the form of keywords from SMS messages sent and received by ad hoc location-based communities of users. Trends are then presented to the user using a phone widget, which is regularly updated to show
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