The following code illustrates the mechanism by which we can aggregate Tweets based on sentiment. The aggregation process is based on the association of tweets with the same feelings, as well as the degree and proportion of the feeling. The methodology used is based on building a recurrent neural network capable of analyzing sentiment, using a data set that includes a number of emotions. The next stage involves using the trained model to sort tweets based on sentiment with a rating ratio. In this partial stage, we will follow two methodologies: The first is to draw a graph that shows the percentage of each of the feelings of the tweeters within Twitter regarding what is happening in the state of Sri Lanka. The next partial stage, is to move to the study of each of these feelings for the tweeters, and try to collect them in order to determine the degree of feelings for each of them. The final hierarchical schemas (for each one of the feelings) will show the correlation of the tweeters in terms of the degree of affiliation with that feeling. The Euclidean distance was used to calculate the degree of convergence for a single feeling (depending on the percentage of tweeting classification and belonging to a specific feeling).
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View Code? Open in Web Editor NEWThe following code illustrates the mechanism by which we can aggregate Tweets based on sentiment. The aggregation process is based on the association of tweets with the same feelings, as well as the degree and proportion of the feeling. The methodology used is based on building a recurrent neural network capable of analyzing sentiment, using a data set that includes a number of emotions. The next stage involves using the trained model to sort tweets based on sentiment with a rating ratio. In this partial stage, we will follow two methodologies: The first is to draw a graph that shows the percentage of each of the feelings of the tweeters within Twitter regarding what is happening in the state of Sri Lanka. The next partial stage, is to move to the study of each of these feelings for the tweeters, and try to collect them in order to determine the degree of feelings for each of them. The final hierarchical schemas (for each one of the feelings) will show the correlation of the tweeters in terms of the degree of affiliation with that feeling. The Euclidean distance was used to calculate the degree of convergence for a single feeling (depending on the percentage of tweeting classification and belonging to a specific feeling).