Multimodal Sentiments: Unraveling Text and Emoji Dynamics Through Deep Learning

The growth of the internet has made it possible for people to use text messaging programs to communicate and share their thoughts about everyday activities as well as regional and national events. This overview examines how text mining is developing, with a focus on sentiment analysis and the newly-emerging discipline of emotion detection. It primarily focuses on the use of deep learning for code-mixed or Hinglish languages in sentiment analysis and emotion identification. This research looks at study designs, state-of-the-art models at the moment, and how well deep learning interprets data with mixed text and emojis. Insights into modern sentiment analysis are sought by shedding light on the complex interplay between textual and visual clues, with a particular emphasis on developmental stages, obstacles, and future prospects. The study provides direction for further study and advancement in the areas of multimodal sentiment analysis and emotion recognition in Hinglish or code mixed data. To further improve our comprehension of this field, a bibliometric analysis is also carried out to pinpoint the most pertinent sources, trends, and geographic patterns.