چکیده (انگلیسی):

As the popularity of Twitter continues to increase rapidly, it is extremely necessary to analyze the huge
amount of data that Twitter users generate. A popular method of tweet analysis is clustering. Because most
tweets are textual, this study focuses on clustering tweets based on their textual content similarity. This
study presents tweet clustering using cellular genetic algorithm cGA. The results obtained by cGA are
compared with those obtained by generational genetic algorithm in terms of average fitness, average time
required for execution and number of generations. Experimental results are tested with two sets: One of
1000 tweets and the second formed of 5000 tweets. The results show a nearly equal performance for both
algorithms in terms of the average fitness of the solution. On the other hand, cGA shows a much faster
performance than generational. These results demonstrate that cellular genetic algorithm outperforms
generational genetic algorithm in tweet clustering.