Social media sentiment analysis through parallel dilated convolutional neural network for smart city applications

Deep Learning is considered to leverage smart cities through social media sentiment analysis. The digital content in social media can be used for many smart city applications (SCAs)(1). Classical convolutional neural networks (CNNs) are challenging to parallelize and insufficient to capture long term contextual semantic features for sentiment analysis. In this perspective, this paper initially proposes a domain-specific distributed word representation (DS-DWR)(2 )with a considerably small corpus size induced from textual resources in social media. In DS-DWR, different Distributed Word Representations are concatenated to builds rich representations over the input sequence, which is worthwhile for infrequent and unseen terms. Second, a dilated convolutional neural network (D-CNN)(3), which is composed of three parallel dilated convolutional neural network (PDCNN)(4) layers and a global average pooling (GAP)(5) layer. Our considered parallel dilated convolution reduces dimension and incorporates an extension in the size of receptive fields without the loss of local information. Further, the long-term contextual semantic information is achieved by the use of different dilation rates. Experiments demonstrate that our architecture accomplishes comparable results with multiple hyperparameters tuning for better parallelism which leads to the minimized computational cost.