Unlocking Cross-Lingual Sentiment Analysis through Emoji Interpretation: A Multimodal Generative AI Approach
- URL: http://arxiv.org/abs/2412.17255v1
- Date: Mon, 23 Dec 2024 03:57:45 GMT
- Title: Unlocking Cross-Lingual Sentiment Analysis through Emoji Interpretation: A Multimodal Generative AI Approach
- Authors: Rafid Ishrak Jahan, Heng Fan, Haihua Chen, Yunhe Feng,
- Abstract summary: Emojis have become ubiquitous in online communication, serving as a universal medium to convey emotions.
Our study aims to investigate the capacity of emojis to serve as reliable sentiment markers through large language models (LLMs)
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.
- Score: 8.762679920056486
- License:
- 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 large language models (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.
Related papers
- Irony in Emojis: A Comparative Study of Human and LLM Interpretation [53.66354612549173]
This study examines the ability of GPT-4o to interpret irony in emojis.
By prompting GPT-4o to evaluate the likelihood of specific emojis being used to express irony on social media, we aim to bridge the gap between machine and human understanding.
arXiv Detail & Related papers (2025-01-20T03:02:00Z) - Semantics Preserving Emoji Recommendation with Large Language Models [47.94761630160614]
Existing emoji recommendation methods are primarily evaluated based on their ability to match the exact emoji a user chooses in the original text.
We propose a new semantics preserving evaluation framework for emoji recommendation, which measures a model's ability to recommend emojis that maintain the semantic consistency with the user's text.
arXiv Detail & Related papers (2024-09-16T22:27:46Z) - Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social
Media Communications [15.621456693085234]
Emojis have become prevalent in social network communications.
Researchers rely on crowd-sourcing to annotate emojis in order to understand their sentiments, usage intentions, and semantic meanings.
Large Language Models (LLMs) have achieved significant success in various annotation tasks.
This study aims to validate the hypothesis that ChatGPT can serve as a viable alternative to human annotators in emoji research.
arXiv Detail & Related papers (2024-01-22T06:02:39Z) - Human vs. LMMs: Exploring the Discrepancy in Emoji Interpretation and Usage in Digital Communication [68.40865217231695]
This study examines the behavior of GPT-4V in replicating human-like use of emojis.
The findings reveal a discernible discrepancy between human and GPT-4V behaviors, likely due to the subjective nature of human interpretation.
arXiv Detail & Related papers (2024-01-16T08:56:52Z) - EmojiLM: Modeling the New Emoji Language [44.23076273155259]
We develop a text-emoji parallel corpus, Text2Emoji, from a large language model.
Based on the parallel corpus, we distill a sequence-to-sequence model, EmojiLM, which is specialized in the text-emoji bidirectional translation.
Our proposed model outperforms strong baselines and the parallel corpus benefits emoji-related downstream tasks.
arXiv Detail & Related papers (2023-11-03T07:06:51Z) - Emoji-aware Co-attention Network with EmoGraph2vec Model for Sentiment
Anaylsis [9.447106020795292]
We propose a method to learn emoji representations called EmoGraph2vec and design an emoji-aware co-attention network.
Our model designs a co-attention mechanism to incorporate the text and emojis, and integrates a squeeze-and-excitation block into a convolutional neural network.
Experimental results show that the proposed model can outperform several baselines for sentiment analysis on benchmark datasets.
arXiv Detail & Related papers (2021-10-27T08:01:10Z) - Emoji-based Co-attention Network for Microblog Sentiment Analysis [10.135289472491655]
We propose an emoji-based co-attention network that learns the mutual emotional semantics between text and emojis on microblogs.
Our model adopts the co-attention mechanism based on bidirectional long short-term memory incorporating the text and emojis, and integrates a squeeze-and-excitation block in a convolutional neural network to increase its sensitivity to emotional semantic features.
arXiv Detail & Related papers (2021-10-27T07:23:18Z) - Black or White but never neutral: How readers perceive identity from
yellow or skin-toned emoji [90.14874935843544]
Recent work established a connection between expression of identity and emoji usage on social media.
This work asks if, as with language, readers are sensitive to such acts of self-expression and use them to understand the identity of authors.
arXiv Detail & Related papers (2021-05-12T18:23:51Z) - Semantic Journeys: Quantifying Change in Emoji Meaning from 2012-2018 [66.28665205489845]
We offer the first longitudinal study of how emoji semantics changes over time, applying techniques from computational linguistics to six years of Twitter data.
We identify five patterns in emoji semantic development and find evidence that the less abstract an emoji is, the more likely it is to undergo semantic change.
To aid future work on emoji and semantics, we make our data publicly available along with a web-based interface that anyone can use to explore semantic change in emoji.
arXiv Detail & Related papers (2021-05-03T13:35:10Z) - Are Emojis Emotional? A Study to Understand the Association between
Emojis and Emotions [37.86739837901986]
We seek to explore the connection between emojis and emotions by means of a new dataset consisting of human-solicited association ratings.
We additionally conduct experiments to assess to what extent such associations can be inferred from existing data, such as similar associations can be predicted for a larger set of emojis.
arXiv Detail & Related papers (2020-05-02T04:04:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.