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interest for people so in this paper an easy approach of gathering and analyzing data through keyword based search in social networks is examined using NodeXL and data is gathered from twitter in which political trends have been analyzed. As a result it will be analyzed that, what people are focusing most in politics.
event can be effortlessly found using keyword matching, but there are numerous tweets that are likely to contain information that is semantically identical. Moreover, there exist many systems for recapitulating tweets related to a particular event, but they have numerous limitations and are unable to provide accurate
measure sentiment using a binary choice keyword algorithm and a multi-knowledge based approach is proposed using, Self-Organizing Maps and tourism domain knowledge in order to model sentiment. We develop a visual model to express this taxonomy of sentiment vocabulary and then apply this model to maximums and minimums in the
This paper describes a methodology approach and a tool dedicated to the exploration of the Twitter social stream by combining different contextual parameters such as time, keywords, gender or the opinion. The exploration can be made in two main modes depending on the fact that the phenomenon is either known or not
on pre-defined analysis operators which exploit keywords available in the entity view together with similarity information to produce summary information about the view contents from both a thematic and analytics perspective. In particular, smart entity views can be analyzed according to the following exploratory
feeling or emotions. To deal with the author's feelings, we suggest enhancing a text tweet with an appropriate image, along with/without text. To generate an image from the text, we first analyze the text tweet. The morpheme analyzer detects the key words and then the thumbnail images related to those keywords are retrieved
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