  The emoticons are symbolic representations that generally accompany the
textual content to visually enhance or summarize the true intention of a
written message. Although widely utilized in the realm of social media, the
core semantics of these emoticons have not been extensively explored based on
multiple modalities. Incorporating textual and visual information within a
single message develops an advanced way of conveying information. Hence, this
research aims to analyze the relationship among sentences, visuals, and
emoticons. For an orderly exposition, this paper initially provides a detailed
examination of the various techniques for extracting multimodal features,
emphasizing the pros and cons of each method. Through conducting a
comprehensive examination of several multimodal algorithms, with specific
emphasis on the fusion approaches, we have proposed a novel contrastive
learning based multimodal architecture. The proposed model employs the joint
training of dual-branch encoder along with the contrastive learning to
accurately map text and images into a common latent space. Our key finding is
that by integrating the principle of contrastive learning with that of the
other two branches yields superior results. The experimental results
demonstrate that our suggested methodology surpasses existing multimodal
approaches in terms of accuracy and robustness. The proposed model attained an
accuracy of 91% and an MCC-score of 90% while assessing emoticons using the
Multimodal-Twitter Emoticon dataset acquired from Twitter. We provide evidence
that deep features acquired by contrastive learning are more efficient,
suggesting that the proposed fusion technique also possesses strong
generalisation capabilities for recognising emoticons across several modes.
