One emoji, many meanings: A corpus for the prediction and disambiguation of emoji sense

Shardlow, Matthew; Gerber, Luciano; Nawaz, Raheel

doi:10.1016/j.eswa.2022.116862

Cited by 10 publications

(2 citation statements)

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“…There are also other research topics on emojis, such as emoji prediction and sense disambiguation. Shardlow et al [16] developed a semantic network for emojis to examine their multiple meanings or polysemous nature by analyzing a corpus of tweets and using a deep learning-based disambiguation algorithm. The proposed network identified separable information among different classes of emoji senses, paving the way for improving emoji prediction and disambiguation in natural language processing systems.…”

Section: Introductionmentioning

confidence: 99%

Emoji’s sentiment score estimation using convolutional neural network with multi-scale emoji images

Kulkongkoon,

Cooharojananone,

Lipikorn

2024

IJECE

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Emojis are any small images, symbols, or icons that are used in social media. Several well-known emojis have been ranked and sentiment scores have been assigned to them. These ranked emojis can be used for sentiment analysis; however, many new released emojis have not been ranked and have no sentiment score yet. This paper proposes a new method to estimate the sentiment score of any unranked emotion emoji from its image by classifying it into the class of the most similar ranked emoji and then estimating the sentiment score using the score of the most similar emoji. The accuracy of sentiment score estimation is improved by using multi-scale images. The ranked emoji image data set consisted of 613 classes with 161 emoji images from three different platforms in each class. The images were cropped to produce multi-scale images. The classification and estimation were performed by using convolutional neural network (CNN) with multi-scale emoji images and the proposed voting algorithm called the majority voting with probability (MVP). The proposed method was evaluated on two datasets: ranked emoji images and unranked emoji images. The accuracies of sentiment score estimation for the ranked and unranked emoji test images are 98% and 51%, respectively.

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Section: Introductionmentioning

confidence: 99%

Emoji’s sentiment score estimation using convolutional neural network with multi-scale emoji images

Kulkongkoon,

Cooharojananone,

Lipikorn

2024

IJECE

View full text Add to dashboard Cite

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“…Just as with ambiguous words, context is key for disambiguating eymous emoji. Attempts at machine disambiguation of emoji in context have been successful (Shardlow, Gerber, & Nawaz, 2022; Wijeratne, Balasuriya, Sheth, & Doran, 2016) and human emoji judgments are context‐dependent as well (Miller et al., 2017; Weissman, 2019a). People can use context to figure out which sense of a word is meant in a sentence (even in cases of homophonous emoji, like a palm tree substituting in for palm of a hand, Scheffler, Brandt, Fuente, & Nenchev, 2022); however, the dominance effect that manifests as a processing advantage for one meaning over another demonstrates that context does not tell the entire story here.…”

Section: Introductionmentioning

confidence: 99%

The Lexicon of Emoji? Conventionality Modulates Processing of Emoji

et al. 2023

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Emoji have been ubiquitous in communication for over a decade, yet how they derive meaning remains underexplored. Here, we examine an aspect fundamental to linguistic meaning‐making: the degree to which emoji have conventional lexicalized meanings and whether that conventionalization affects processing in real‐time. Experiment 1 establishes a range of meaning agreement levels across emoji within a population; Experiment 2 measures accuracy and response times to word‐emoji pairings in a match/mismatch task. In this experiment, we found that accuracy and response time both correlated significantly with the level of population‐wide meaning agreement from Experiment 1, suggesting that lexical access of single emoji may be comparable to that of words, even out of context. This is consistent with theories of a multimodal lexicon that stores links between meaning, structure, and modality in long‐term memory. Altogether, these findings suggest that emoji can allow a range of entrenched, lexicalized representations.

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Research on Emotional Perception of Game Characters Based on Semantic Network Analysis

Zhu,

Wu,

Sun

2024

Lecture Notes in Computer Science

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One emoji, many meanings: A corpus for the prediction and disambiguation of emoji sense

Cited by 10 publications

References 10 publications

Emoji’s sentiment score estimation using convolutional neural network with multi-scale emoji images

Emoji’s sentiment score estimation using convolutional neural network with multi-scale emoji images

The Lexicon of Emoji? Conventionality Modulates Processing of Emoji

Research on Emotional Perception of Game Characters Based on Semantic Network Analysis

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