Sentiment analysis has come long way since it was introduced as a natural language processing task nearly 20 years ago. Sentiment analysis aims to extract the underlying attitudes and opinions toward an entity. It has become a powerful tool used by governments, businesses, medicine, marketing, and others. The traditional sentiment analysis model focuses mainly on text content. However, technological advances have allowed people to express their opinions and feelings through audio, image and video channels. As a result, sentiment analysis is shifting from unimodality to multimodality. Multimodal sentiment analysis brings new opportunities with the rapid increase of sentiment analysis as complementary data streams enable improved and deeper sentiment detection which goes beyond text-based analysis. Audio and video channels are included in multimodal sentiment analysis in terms of broadness. People have been working on different approaches to improve sentiment analysis system performance by employing complex deep neural architectures. Recently, sentiment analysis has achieved significant success using the transformer-based model. This paper presents a comprehensive study of different sentiment analysis approaches, applications, challenges, and resources then concludes that it holds tremendous potential. The primary motivation of this survey is to highlight changing trends in the unimodality to multimodality for solving sentiment analysis tasks.

Before the arrival of the web as a corpus, people detected positive and negative news based on the understanding of the textual content from physical newspaper rather than an automatic identification approach from readily available e-newspapers. Thus, the earlier sentiment analysis approach is based on unimodal data, and less effort is paid to the multimodal data. However, the presence of multimodal information helps us to get a clearer understanding of the sentiment. To the best of our knowledge, less work has been introduced on the image–text multimodal sentiment analysis framework of Assamese, a low-resource Indian language mostly spoken in the northeast part of India. We built an Assamese news articles dataset consisting of news text and associated images and one image caption to conduct an experimental study. Focusing on important words and discriminative regions of the images mostly related to sentiment, two individual unimodal such as textual and visual models are proposed. The visual model is developed using an encoder-decoder–based image caption generation system. An image–text multimodal approach is proposed to explore the internal correlation between textual and visual features for joint sentiment classification. Finally, we propose the multimodal sentiment analysis framework, i.e., Textual Visual Multimodal Fusion, by employing a late fusion scheme to merge the three different modalities for the final sentiment prediction. Experimental results conducted on the Assamese dataset built in-house demonstrate that the contextual integration of multimodal features delivers better performance than unimodal features.