Sentiment analysis: a convolutional neural networks perspective

With the dramatic growth of various social media platforms, sentiment analysis (SA) of and emotion detection (ED) in various social network posts, blogs, and conversations are very useful and effective for mining the true opinions on different issues, entities, or aspects. During the last decade, many statistical and probabilistic models based on lexical and machine learning approaches have been employed for these tasks. Majority of the relevant literature has emphasized on improving the contemporary SA determination and emotion extraction techniques. With the recent advancements in deep neural networks, various deep learning models have been heavily used to enhance the accuracy of SA. Convolutional neural networks (CNN), a deep neural network model formerly adopted for visual data processing only, has recently gained acceptance for textual inputs as well. As the inputs for SA may be textual, visual, or any combination of these, CNN seems to be a powerful tool. Capturing spatial and contextual information in an incremental fashion respectively from visual and textual inputs proves CNN as an effective model for SA. In this paper, we present an extensive survey that covers the applicability, challenges, and issues for textual, visual, and multimodal SA using CNNs. A detailed discussion and analysis for SA using a CNN model is summarized. For both of the unimodal inputs i.e., textual and visual, we present an optimized algorithmic approach for SA determination using CNN.