**6. Conclusions and future work**

The sentiment-based semantic rule learning for improved product recommendations is presented. The role of semantic rules in sentiment learning is discussed. The influence of sentiments on semantic rules is also discussed. The algorithms for learning taxonomical and non-taxonomical constraints are explained and results are tabulated. Also the algorithm for improving product sentiments using the learned taxonomical and nontaxonomical constraints for product recommendations is explained and results are tabulated. The design decisions in the implementation of PROO ontology are discussed. Several observations from the experiment are also discussed.

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Future scope of work is in the lines of learning the intentions of the reviewers using the advanced machine learning algorithms and bigger datasets. The influence of the intentions on new customers and on the product manufacturers by quantifying the effect of intention on information diffusion in social media are to be investigated. The classification performance of the machine learning model on the intentions is to be examined for discovering the actual intention of the reviewer.
