Abstract
Numerous viewers choose to watch political or presidential debates highlights via TV or internet, rather than seeing the whole debate nowadays, which requires a lot of time. However, the task of making a debate summary, which can be considered neutral and does not give out a negative nor a positive image of the speaker, has never been an easy one, due to personal or political beliefs bias of the video maker. This study came up with a solution that generates highlights of a political event, based on twitter social network flow. Twitter streaming API is used to detect an event's tweets stream using specific hashtags, and detect on a timescale the extreme changes of volume of tweets, which will determine the highlight moments of our video summary at first, then a process is set up based on a group of ontologies that analyze each tweet of these moments to calculate the percentage of each sentiment’s positivity, then classify those moments by category (positive, negative or neutral).
Highlights
In the 2017 Republicans primaries, CNN claimed that more than 84 million people have watched the republican candidates debating on its channel, breaking records for most events seen on CNN
Several studies gave an interesting insights about the social network twitter evolution due to his dynamic nature with more than 400 million tweets posted everyday [4], using the hashtags (Significant continuation of characters without space beginning with the sign #, Which refers to a subject and inserted into a message by its author, in order to facilitate the location) helps to look for trending topics and look up thousands of tweet (Table 1)
Twitter users usually respond to political speaker statements or point of views during a political speech, which offers a fertile ground for sentiment analysis [5], due to the outrageous tweets against the opposite political speaker or the encouraging tweets from their supporters, those tweets usually come as a reaction to the big (Good or Bad) moments of the speech, which makes their reactions a good highlights’ indicator for the event
Summary
In the 2017 Republicans primaries, CNN claimed that more than 84 million people have watched the republican candidates debating on its channel, breaking records for most events seen on CNN. FOX cited that more than 83 million have seen the debate between the republican candidates, which made it the most watched event in the history of television. The majority of these audiences are social media users, who respond to every controversial moment [1] on various platforms in real time, such as Twitter, Facebook, Snap, Instagram. In the chapter the KDD (knowledge discovery in databases) process will be discussed This approach utilizes these tweets to score the sentiment’s positivity percentage in them, in order to classify these tweets into positive, negative or neutral and able to determine the nature of the moment. 2018.11.01 -- 2018.11.01 2018.11.01 – 2018.11.30 2018.06.30 – 2018.07.31 2018.02.01 – 2018.02.28 www.ijacsa.thesai.org
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