Multimodal Sentiment Analysis: Addressing Key Issues and Setting up Baselines

Background Sentiment analysis is proven to be very useful tool in many applications regarding social media. This has led to a great surge of research in this field. Hence, in this paper, we compile the baselines for such research. Methods In this paper, we explore three different deeplearning based architectures for multimodal sentiment classification, each improving upon the previous. Further, we evaluate these architectures with multiple datasets with fixed train/test partition. We also discuss some major issues, frequently ignored in multimodal sentiment analysis research, e.g., role of speaker-exclusive models, importance of different modalities, and generalizability. This framework illustrates the different facets of analysis to be considered while performing multimodal sentiment analysis and, hence, serves as a new benchmark for future research in this emerging field. Result We draw a comparison among the methods using empirical data, obtained from the experiments. Conclusions In the future, we plan to focus on extracting semantics from visual features, cross-modal features and fusion,