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We examine whether aggregate daily Twitter keyword volumes over eight months from November 2011 to June 2012 can be used to predict aggregate daily consumer spending as reported by Gallup. We also examine whether Twitter keyword volume improves predictive ability over prediction based solely on current spending
number of keywords for the searches of the events, especially in relation with analytics and searches on SNs for the reflectance of those events. A special attention is given to synonyms.
Twitter, as a social media is a very popular way of expressing opinions and interacting with other people in the online world. When taken in aggregation tweets can provide a reflection of public sentiment towards events. In this paper, we provide a positive or negative sentiment on Twitter posts using a well-known machine learning method for text categorization. In addition, we use manually labeled...
As Internet usage and e-commerce grow, online social media serve as popular outlets for consumers to express sentiments about products. On Amazon, users can tag an album with a keyword, while tweets on Twitter represent a more natural conversation. The differing natures of these media make them difficult to compare
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
synchronized with Twitter messages by specific keywords like “rain” or/and “landslide” to analyse the relationship between environmental phenomena and social responses in heavy rain conditions in the Hiroshima region. The results were analysed and visualized through a geovisualization technique to
trigger keywords and contextual cues. The system was tested on multiple large collections of Dutch tweets. Our experimental results show that our system can successfully analyze messages and recognize threatening content.
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