Application of Deep Learning to Sentiment Analysis for recommender system on cloud
IEEE CITS 2017 - 2017 International Conference on Computer, Information and Telecommunication Systems
Cloud, Deep learning, Performance Evaluation, Recursive Neural Networks, Sentiment Analysis
© 2017 IEEE. Sentiment analysis of short texts like single sentences and reviews available on different social networking sites is challenging because of the limited contextual information. Based on the sentiments and opinions available, developing a recommendation system is an interesting concept, which includes strategies that combine the small text content with prior knowledge. In this paper, we explore a new application of Recursive Neural Networks (RNN) with deep learning system for sentiment analysis of reviews. The proposed RNN-based Deep-learning Sentiment Analysis (RDSA) recommends the places that are near to the user's current location by analyzing the different reviews and consequently computing the score grounded on it. Deep Learning is used to optimize the recommendations depending on the sentiment analysis performed on the different reviews, which are taken from different social networking sites. The Experiments performed indicate that the RNN based Deep-learning Sentiment Analysis (RDSA) improvises the behavior by increasing the accuracy of the sentiment analysis, which in turn yields better recommendations to the user and thus helps to identify a particular position as per the requirement of the user need.
Preethi, G.; Krishna, P. Venkata; Obaidat, Mohammad S.; Saritha, V.; and Yenduri, Sumanth, "Application of Deep Learning to Sentiment Analysis for recommender system on cloud" (2017). Faculty Bibliography. 2919.