This dataset provides a detailed analysis of emoji usage across various social media platforms. It captures how different emojis are used in different contexts, reflecting emotions, trends, and user demographics.

With emojis becoming a universal digital language, this dataset helps researchers, marketers, and data analysts explore how people express emotions online and identify patterns in social media communication.

📌 Key Features: 😊 Emoji Details: Emoji 🎭: The specific emoji used in a post, comment, or message. Context 💬: The meaning or emotion associated with the emoji (e.g., Happy, Love, Funny, Sad). Platform 🌐: The social media platform where the emoji was used (e.g., Facebook, Instagram, Twitter). 👤 User Demographics: User Age 🎂: Age of the user who posted the emoji (ranges from 13 to 65 years). User Gender 🚻: Gender of the user (Male/Female). 📈 Additional Insights: Emoji Popularity 🔥: Frequency of each emoji’s usage across platforms. Trends Over Time 📅: How emoji usage changes based on trends or events. Regional Usage Patterns 🌍: How different cultures and regions use emojis differently. 📊 Use Cases & Applications: 🔹 Understanding emoji trends across social media 🔹 Analyzing emotional expression through digital communication 🔹 Exploring demographic differences in emoji usage 🔹 Identifying platform-specific emoji preferences 🔹 Enhancing sentiment analysis models with emoji insights

⚠️ Important Note: This dataset is synthetically generated for educational and analytical purposes. It does not contain real user data but is designed to reflect real-world trends in emoji usage.

The twitter emoji dataset obtained from CodaLab comprises of 50 thousand tweets along with the associated emoji label. Each tweet in the dataset has a corresponding numerical label which maps to a specific emoji. The emojis are of the 20 most frequent emojis and hence the labels range from 0 to 19

valhalla/emoji-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset contains 1500+ emojis with their Arabic descriptions, sentiment classifications (Positive, Negative, Mixed), and Unicode representations. It is useful for NLP tasks, sentiment analysis, emoji-based chatbots, and AI language models supporting Arabic text. The dataset has been refined to correct Arabic text and remove inappropriate words.

Key Features:

📝 1500+ emojis with detailed Arabic meanings 📊 Sentiment labels (Positive, Negative, Mixed) 🔤 Unicode representation for easy integration ✅ Cleaned & filtered to improve readability and avoid inappropriate terms 💡 Useful for AI, machine learning, and chatbot training

Original Data Source: Arabic Emoji Meanings Dataset – 1500+ Emojis with

some Unicode of emojis

Collection of 13M tweets divided into training, validation, and test sets for the purposes of predicting emoji based on text and/or images.

The data provides the tweet status ID and the emoji annotations associated with it. In the case of image-containing subsets, the image URL is also listed.

The Full, unbalanced dataset consists of a random test and validation sets of 1M tweets, with the remainder in the training set.

The Balanced testset is a subset of the test set chosen to improve emoji class balance.

The Image subsets are image-containing tweets.

Finally, emoji_map_1791.csv provides information regarding the emoji labels and potential metadata.

URL to get the tweet based on ID: `https://twitter.com/anyuser/status/

A lexicon of 751 emoji characters with automatically assigned sentiment. The sentiment is computed from 70,000 tweets, labeled by 83 human annotators in 13 European languages. The Emoji Sentiment Ranking web page at http://kt.ijs.si/data/Emoji_sentiment_ranking/ is automatically generated from the data provided in this repository. The process and analysis of emoji sentiment ranking is described in the paper: P. Kralj Novak, J. Smailović, B. Sluban, I. Mozetič, Sentiment of Emojis, submitted; arXiv preprint, http://arxiv.org/abs/1509.07761, 2015.

📊 Dataset Overview

The emoji-map dataset, created by omarkamali, contains text data in parquet format. It consists of 10K-100K entries, specifically 5.03k rows. The dataset is available in the train split.

  📁 Data Structure

The dataset includes two main columns: emoji and unicode_description. The emoji column contains various emoji characters, while the unicode_description column provides a textual description of each emoji.

  🔍 Sample Data

Examples from the… See the full description on the dataset page: https://huggingface.co/datasets/omarkamali/emoji-map.

Emojis are used in online communication to convey expression and emotion. This study investigated whether emoji integration occurs at an early stage of reading or at a late, more conscious stage. Participants' eye movements were monitored as they read informal, text-message-style sentences containing either a contextually congruent face emoji, a contextually incongruent face emoji, or a dash. Comprehension questions were included after each message to encourage reading for comprehension. Three early (skipping rate, first fixation duration, gaze duration) and three late (total reading time, regression in probability, trial dwell time) processing measures were analysed. Results revealed that compared with message-congruent emojis, incongruent emojis incurred significant processing costs on all late measures and one early measure (gaze duration). Further, both emoji conditions showed higher skipping rates and longer reading times relative to the dash trials across most measures, indicating emoji processing costs during both early and late stages of reading.

This dataset includes material collection, experimental procedures and experimental data (raw data and data used for analyses).

Dataset

This dataset was created by Fares Hazem

Released under Apache 2.0

Contents

Reaction time and accuracy data for colour emoji categorisation task

This is the documentation of the tomographic X-ray data of emoji
phantom made available at http://www.fips.fi/dataset.php. The data can be freely used for scienti c purposes with appropriate references to the data and to this document in http://arxiv.org/. The data set consists of (1) the X-ray sinogram of a single 2D slice of 33 emoji faces (contains 15 different emoji faces) made by small squared ceramic stones and (2) the corresponding static and dynamic measurement matrices modeling the linear operation of the X-ray transform. Each of these sinograms was obtained from a measured 60-projection fan-beam sinogram by down-sampling and taking logarithms. The original (measured) sinogram is also provided in its original form and resolution. The original (measured) sinogram is also provided in its original form and resolution.

2) emoticon parts automatically divided from raw emoticons into semantic areas representing “mouths” or “eyes”.

Reaction time and accuracy data from a categorisation task of emojis. The emojis were positive, negative, and neutral valence emojis presented on red, green, blue, grey, or white backgrounds.

Facial emotion recognition deficits are common after moderate-severe traumatic brain injury (TBI) and linked to poor social outcomes. We examine whether emotion recognition deficits extend to facial expressions depicted by emoji. Fifty-one individuals with moderate-severe TBI (25 female) and fifty-one neurotypical peers (26 female) viewed photos of human faces and emoji. Participants selected the best-fitting label from a set of basic emotions (anger, disgust, fear, sadness, neutral, surprise, happy) or social emotions (embarrassed, remorseful, anxious, neutral, flirting, confident, proud). We analyzed the likelihood of correctly labeling an emotion by group (neurotypical, TBI), stimulus condition (basic faces, basic emoji, social emoji), sex (female, male), and their interactions. Participants with TBI did not significantly differ from neurotypical peers in overall emotion labeling accuracy. Both groups had poorer labeling accuracy for emoji compared to faces. Participants with TBI (but not neurotypical peers) had poorer accuracy for labeling social emotions depicted by emoji compared to basic emotions depicted by emoji. There were no effects of participant sex. Because emotion representation is more ambiguous in emoji than human faces, studying emoji use and perception in TBI is an important consideration for understanding functional communication and social participation after brain injury.

This is the data for paper: Can Emoji Promote Forgiveness? The Relationship between Emoji Use, Empathy, Attribution of Responsibility, and Forgiveness in Apologies. A total of 323 participants were recruited in that study, and a recall method (Study 1) and scenario simulation method (Study 2) were used to explore the effect of emoji use during apologies on forgiveness, and the mediating role of empathy and attribution of responsibility. The results showed that (a) people chose emoji that resembled real remorseful facial expressions when apologizing; (b) using emoji that expressed remorse when apologizing could promote forgiveness; and (c) empathy mediated the process of emoji promoting forgiveness, while attribution of responsibility did not play a mediating role.

This dataset contains 100d word embeddings trained on 48M Italian tweets using fastText and employed by our team to predict emojis during ITAmoji competition of EVALITA 2018 Evaluation Campaign.

In a context where there is permanent electoral campaigning, an increasing number of political communication experts are trying to unravel the resources used by government officials and their parties to influence TikTok users. From a broad perspective, the subject matter is not new, but it is topical; nonetheless, this research discloses a gap in the literature by amalgamating the recognition of idiosyncratic attributes of the feminisation of political discourse on TikTok with the analysis of the reactions (text and emojis) that the audiovisual content imbued by this trend elicits in users. The purpose is to ascertain whether the inclusive tone of the feminised rhetorical style can be extrapolated to TikTok and, if so, whether its particular characteristics mitigate expressions of incivility. To do so, the initial content posted (first seven months) on TikTok by the Spanish political platform Sumar with its leader, Yolanda Díaz, featuring prominently in most of the videos, were selected for scrutiny. A mixed methodology analysis of audiovisual content and comments showed that the anti-polarisation rhetoric and storytelling contributed to neutralising the extreme forms of flaming, although Sumar did not use a strategy tailor-made to suit TikTok.

The NCA Calendar Dataset is part of the Neural Cellular Automata Emoji Challenge and contains animated gifs used as content for each day in the NCA emojis Advent Calendar Julekalender notebooks