Please use this identifier to cite or link to this item:
Title: A personalized recommender system using Machine Learning based Sentiment Analysis over social data
Authors: Ashok, M.
Rajanna, S.
Joshi, P.V.
Sowmya, Kamath S.
Issue Date: 2016
Citation: 2016 IEEE Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2016, 2016, Vol., , pp.-
Abstract: 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. � 2016 IEEE.
Appears in Collections:2. Conference Papers

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.