Relevance Feedback on Mobile Data Using RapidMiner

Relevance feedback is an essential feature that improves the final retrieved results. The working efficiency of the relevance feedback depends on feedback information given by the user each time the system gets some retrieved set of results. In order to improve the relevance feedback information, we have applied algorithms on the dataset and prepared a model. Sentiment analysis is further applied on the dataset to transform it into an accurate form and then predict the nature that has been classified into three: positive, negative or neutral sentiments. Decision tree model is used for the dataset after which it is further processed for testing and validation. In the last, a model is prepared that can, not only justify the sentiments on the basis of positive, negative and neutral but also calculates the accuracy of analyzing the statements which can act as a great help for relevance feedback systems.