Özet:

Today, due to the intense use of social media platforms such as Twitter by all segments of today's technology, people have begun to share their views, ideas, and feelings through these media. It is possible to discover mighty valuable knowledge from this enormous resource. This study has emerged to assist users in making choices by evaluating emotions about TV series and movies that have recently appeared on social platforms, using ideas and feelings. The textual tweet data was preprocessed and cleaned of noise by using natural language processing techniques. Tweets were tagged using the Bert-based model according to the content of the Turkish TV series and movie comments, and their polarities were calculated. Machine learning models including Naïve Bayes (NB), Support Vector Machines (SVM), Random Forest (RF); Bagging and Voting, which are among the general ensemble algorithms, were trained for sentiment analysis by taking the obtained polarity values. The voting algorithm gives the best accuracy at 87%, while the Support Vector Machines give the best area under the receiver operating characteristics curve (AUC) of 0.96. A web application was developed by using Flask to monitor sentiment scores via hashtags (#).

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