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A personalized recommender system using Machine Learning based Sentiment Analysis over social data

Social Media platforms are already an indispensable part of our daily lives. With its constant growth, it has contributed to superfluous, heterogeneous data which can be overwhelming due to its volume and velocity, thus limiting the availability of relevant and required information when a particular query is to be served. Hence, a need for personalized, fine-grained user preference-oriented framework for resolving this problem and also, to enhance user experience is increasingly felt. In this paper, we propose a such a social framework, which extracts user's reviews, comments of restaurants and points of interest such as events and locations, to personalize and rank suggestions based on user preferences. Machine Learning and Sentiment Analysis based techniques are used for further optimizing search query results. This provides the user with quicker and more relevant data, thus avoiding irrelevant data and providing much needed personalization.