For Twitter, a hashtag recommendation system is an important tool to organize similar content together for topic categorization. Much research has been carried out on figuring out a new technique for hashtag recommendation, and very little research has been done on evaluating the performance of different existing models
using the same dataset and the same evaluation metrics. This paper evaluates the performance of different content-based methods(Tweet similarity using hashtag frequency, Naïve Bayes model, and KNN-based cosine similarity) for hashtag recommendation using different evaluation metrics including Hit Ratio, a metric recently created for evaluating a hashtag recommendation system. The result shows that Naive Bayes outperforms other methods with an average accuracy score of 0.83.
Software Architecture Diagram
Design of the Web Application