Sentiment Analysis using BLSTM-ResNet on Textual Images

Sentiment Analysis is a popular classification problem where the degree of emotion is analysed based on text. Convolution Neural Network (CNN) are part of various sentiment classification models, but their use has been limited to one-dimensional (1D) convolution or shallow two-dimensional (2D) convolution. The primary focus of this research is to use deep 2D-CNN architectures, inspired by the popular computer vision model ResNet, to replace a shallow 2D-CNN in an existing BLSTM (Bidirectional Long Short Term Memory)-2DCNN model. This research investigates a new method for sentiment analysis which is an amalgam of the practices popular in Natural Language Processing (NLP) as well as Computer Vision, striving to extract opinions from a text by transforming text into images. The text images are formed by transforming the word embeddings matrix into a greyscale image with the help of BLSTM cell. Intensive experiments have been conducted on the Sentiment140 dataset with our novel BLSTM-ResNet model, which contains residual blocks with skip connections. Several BLSTM-ResNet variants were tested to investigate the impact of network depth and dataset size on sentiment detection. Moreover, two sets of residual blocks are designed to form our shallow and deep BLSTM-ResNet models. Our best shallow BLSTM-ResNet models have achieved 4.06% and 3.43% increases in accuracy for dataset sizes 80,000 and 200,000 respectively, compared with the baseline BLSTM-2DCNN model. In addition, an overall improvement is observed on accuracy with every additional residual block in our shallow BLSTM-ResNet model until accuracy saturates, and the same trend has been seen on the impact of dataset size on the performance. Our deep BLSTM-ResNet models show the same positive impact of network depth impact and dataset size on sentiment analysis. Further investigation on the shallow and deep BLSTM-ResNet models shows that deep BLSTM-ResNet outperforms shallow BLSTM-ResNet, in general.