Sentiment analysis through recurrent variants latterly on convolutional neural network of Twitter

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 light of substantial twitter corpora to deal with this problem. Experimentations exhibit better execution when it is compared with the baseline model on the twitter's corpora which tends to perform dependable results for the analysis of sentiment benchmarks by achieving 90.59% on Stanford Twitter Sentiment Corpus, 89.46% on Sentiment Strength Twitter Data and 88.72% on Health Care Reform Dataset respectively. Empirically, our work turned to be an efficient architecture with slight hyperparameter tuning which capable us to reduce the number of parameters with higher performance and not merely relying on convolutional multiple layers by constructing the RNN layer followed by convolutional layer to seizure long-term dependencies.