Unlocking Cross-Lingual Sentiment Analysis through Emoji Interpretation: A Multimodal Generative AI Approach (2412.17255v1)

Abstract: Emojis have become ubiquitous in online communication, serving as a universal medium to convey emotions and decorative elements. Their widespread use transcends language and cultural barriers, enhancing understanding and fostering more inclusive interactions. While existing work gained valuable insight into emojis understanding, exploring emojis' capability to serve as a universal sentiment indicator leveraging LLMs has not been thoroughly examined. Our study aims to investigate the capacity of emojis to serve as reliable sentiment markers through LLMs across languages and cultures. We leveraged the multimodal capabilities of ChatGPT to explore the sentiments of various representations of emojis and evaluated how well emoji-conveyed sentiment aligned with text sentiment on a multi-lingual dataset collected from 32 countries. Our analysis reveals that the accuracy of LLM-based emoji-conveyed sentiment is 81.43%, underscoring emojis' significant potential to serve as a universal sentiment marker. We also found a consistent trend that the accuracy of sentiment conveyed by emojis increased as the number of emojis grew in text. The results reinforce the potential of emojis to serve as global sentiment indicators, offering insight into fields such as cross-lingual and cross-cultural sentiment analysis on social media platforms. Code: https://github.com/ResponsibleAILab/emoji-universal-sentiment.

Unlocking Cross-Lingual Sentiment Analysis through Emoji Interpretation: A Multimodal Generative AI Approach

This paper presents a novel exploration into the cross-lingual sentiment analysis capabilities of emojis through the integration of generative AI methodologies. Emojis, often viewed as a universal mode of communication, have permeated digital interactions globally, transcending linguistic and cultural barriers. While prior studies have focused on understanding emojis in a limited linguistic context, the present research embarks on an expansive examination of emojis as standalone indicators of sentiment across various languages and cultures.

The paper leverages multimodal capabilities of LLMs, specifically ChatGPT, to investigate how well the sentiment conveyed by emojis aligns with text sentiment. The researchers collected a multilingual dataset comprising texts from 32 countries and representing 19 languages, allowing for a thorough cross-cultural analysis. The key objective was to evaluate the accuracy of sentiment ascribed to various emoji representations using LLMs and to establish emojis as reliable sentiment markers independent of accompanying text.

A rigorous methodology was employed to achieve these objectives. The researchers began by generating a comprehensive dataset of emoji representations covering icons, descriptions, and pixel-level designs, ensuring a robust foundation for analysis. They explored 15 different combinations of these representations, identifying the combination of pixel, icon, and description as providing the best performance in sentiment classification, achieving an impressive accuracy of 81.43%.

Furthermore, the paper introduces algorithms for standalone sentiment analysis based on emojis, including Basic Sentiment Aggregation (BSA), Dual Positive Model (DPM), and a majority voting scheme. Notably, the performance of these algorithms was enhanced by considering the positional significance of emojis within text, with prioritization of the first emoji proving particularly effective. Compared to non-position-aware approaches, this enhancement offers a refined understanding of emoji sentiment accuracy with an observed increase of several percentage points.

The implications of these findings are significant for both theoretical and practical applications. The research highlights the potential of emojis as universal sentiment indicators, which could revolutionize cross-lingual and cross-cultural sentiment analysis, offering valuable insights into social media analytics and other domains relying on sentiment detection.

The paper opens avenues for future research in generative AI and multimodal sentiment analysis. Prospective work could explore the domain of low-resource languages, where language-specific sentiment models traditionally fall short. Additionally, the impact of contextual variations, both visual and semantic, of emojis across different cultural landscapes presents a fertile ground for further inquiry.

By underscoring the utility of multimodal AI approaches in bridging linguistic diversity, this research contributes a substantial advancement to the field of sentiment analysis. Its findings may influence future developments in cross-lingual sentiment analysis frameworks, and it underscores the growing importance of generative AI in enhancing our understanding of non-textual communication forms.