Sentiment Analysis (SA) or Opinion Mining can help in identifying subjective information conveyed by user reviews for various automation tasks such as building better recommendation systems, identifying user trends, monitoring, and customer support. This paper focuses on sentiment score detection. Traditional SA algorithms suffer from low accuracies in identifying true user intents. However, with the advent of Deep Learning many NLP (Natural Language Processing) tasks including Sentiment Analysis have become feasible with accuracies comparable to that of human experts. An additional advantage of Deep Learning in contrast to supervised learning is that in deep learning a manually tuned feature set is not required. Deep Learning algorithm such as Convolution Neural Networks (CNN), Long Short Term Memory (LSTM), Recurrent Neural Networks (RNN)and various other have successfully been applied to SA. RNN, in particular, is well suited for this task, however, most of the works done over RNNs require large supervised training sets which are usually not available for all domains. This work proposes a new method called RNNCore which can make use of the pre-trained word embeddings from Stanford Core NLP in conjunction with RNN to improve accuracy and reduce computation cost. Comparison between the results of RNNCore, RNN, and OneRmethod on the IMDB review dataset suggests that RNNCore yields 92.60%F1-measure which is a marked improvement of 17.74% as compared with a simple RNN approach for Sentiment Analysis.