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Sentiment analysis has been a hot area in the exploration field of language understanding, however, neural networks used in it are even lacking. Presently, the greater part of the work is proceeding on recognizing sentiments by concentrating on syntax and vocabulary. In addition, the task identified with natural language processing and for computing the exceptional and remarkable outcomes Recurrent neural networks (RNNs) and Convolutional neural networks (CNNs) have been utilized. Keeping in mind the end goal to capture the long-term dependencies CNNs, need to rely on assembling multiple layers. In this Paper for the improvement in understanding the sentiments, we constructed a joint architecture which places of RNN at first for capturing long-term dependencies with CNNs using global average pooling layer while on top a word embedding method using GloVe procured by unsupervised learning in the …