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.

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.

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

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

some Unicode of emojis

📊 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.

A table entry is the number of emojis in (∈), or missing (∉) from a data source. N(Single, Double) denotes the total number N of emoji symbols, partitioned into the Single- and Double-character symbols, respectively.

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NotCompany Not-milk product

Context

This dataset is a small step into my quest to find the most nutritious food combination using the emoji diet as example. I made this small dataset trying to answer a question that many companies are nowadays working on, for instance, how do I make a burger without using any animal products?

Content

This dataset contains a brief summary of the nutritional value of emoji foods, including energy, protein, carbs, fats, minerals, and vitamins. I found all the emoji food & drinks on the amazing emojipedia and I hand picked the nutritional value from the USDA Food Composition Database using its food search feature. Then all values were normalized per gram of food using a script on this sheet. I thought this would it be more useful than just different amount of food or 100g per food.

Inspiration

1. What is the perfect emoji food combinations to meet all my daily nutritional needs?
2. How do I make the nutritional combination of a 300g cut of meat 🥩 using just vegetables and fruits fats 🥑, carbs 🥕 and protein 🥜?
3. How do I replace a bacon's nutritional content on a vegetarian diet?

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.

Reaction time and accuracy data for colour emoji categorisation task

Emo Visual Data

Introduction

This is an emoticon visual annotation data set, which collects 5329 emoticons and uses the glm-4v api and step-free-api projects to complete the visual annotation through multi-modal large models.

Example:

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Original 这个表情包中的内容和笑点在于它展示了一只卡通兔子，兔子的表情看起来既无奈又有些生气，配文是“活着已经够累了，上网你还要刁难我”。这句话以一种幽默的方式表达了许多人在上网时可能会遇到的挫折感或烦恼，尤其是当遇到困难或不顺心的事情时。这种对现代生活压力的轻松吐槽使得这个表情包在社交媒体上很受欢迎，人们用它来表达自己在网络世界中的疲惫感或面对困难时的幽默态度。

Translated: The content and laughter of this emoticon package is that it shows a cartoon rabbit. The rabbit's expression looks helpless and a little angry. The caption is "I am tired of living, but you still make things difficult for me online." This quote expresses in a humorous way the frustration or annoyance that many people may experience when surfing the Internet, especially when something difficult or doesn't go their way. This lighthearted take on the pressures of modern life has made the meme popular on social media, where people use it to express their feelings of exhaustion in the online world or to use humor in the face of difficulties.

Data was analyzed using repeated measures to assess the impact of emoji-based feedback on participants’ outcomes across alternating baseline (A) and intervention (B) phases. With 40 participants completing the full ABABAB sequence, the design allowed within-subject comparisons of performance and/or perceptions between conditions. For each outcome variable, means and standard deviations were calculated for each phase (A1, B1, A2, B2, A3, B3). A repeated-measures ANOVA was used to evaluate changes across phases, testing for statistically significant differences between baseline and intervention conditions over time. All analyses were conducted using Python (v3.11) with statistical packages including statsmodels and scipy. Visualizations were generated using seaborn and matplotlib —All code and results were verified and interpreted by the authors. Statistical significance was set at p < .05.

Data post-comment pairs were collected from 13 selected Indonesian public figures (artists) / public accounts with more than 15 million followers and categorized as famous artists. It was collected from Instagram using an online tool and Selenium. Two persons labeled all pair data as an expert in a total of 72874 data. The data contains Unicode text (UTF-8) and emojis scrapped in posts and comments without account profile information.

It contains several fields: -igid: Account ID, -comment: Comment of a post, -post: Post from an ID, -emoji: Whether the data contains emojis or not (1 or 0), -spam: Whether the data is spam or not (1 or 0), -lengthcomment: The character length of the comment, -lengthpost: The character length of the post, -countemojicomment: Number of emoji symbol characters in comments, -countemojicommentuniq: Number of emoji symbol characters in comments (unique), -countemojipost: Number of emoji symbol characters in posts, -countemojipostuniq: Number of emoji symbol characters in the post (unique)

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

The dataset is a TSV (tab-separated) with five columns: the first two columns represent the codes of the pair of emojis evaluated, the third column their gold standard similarity, the fourth column their gold standard relatedness and the fifth column the average of the previous two values. Each row of the file represents the gold standard evaluation results of a pair of emojis. Remember that in order to retrieve the vectorial embedding corresponding to an emoji in our models, you need to add the token "eoji" before the emoji code.

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).

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.

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.

IntroductionEffective communication is essential for building a successful patient–healthcare professional (HCP) relationship. Understanding a patient’s emotional context is key to building this relationship. However, communication barriers often hinder the development of these relationships. Strategies to communicate emotions in healthcare settings may address this issue. Emojis are small images that together form a universal language well-suited to describe emotions.MethodsThis three-phase feasibility study used quantitative and qualitative approaches to explore current opinions around the utility of emojis in expressing emotions during patient–HCP communications. In phase 1, members of the War on Cancer digital community participated in an online survey to determine their use of emojis in personal and healthcare communications. In phase 2, selected patient volunteers were interviewed to further understand the responses from the survey. In phase 3, invited HCPs were interviewed to evaluate their use of digital communications and emojis with patients, and insights on the findings from phases 1 and 2.ResultsIn phase 1, 290 community members responded to the survey (16–84 years old; twenty-two countries). Of these, 70% (n = 197/280) reported common use of emojis in personal conversations, and 62% (n = 158/256) were optimistic about their use in HCP communications. All eight patients interviewed in phase 2 (30–70 years old; three countries) used emojis in personal communications but rarely in healthcare settings. They identified four situations where emojis could be useful in HCP communication: emotional preparation before a visit, follow-up after a visit, situations with a language barrier, and to replace numeric scales when expressing strength of emotion. All five of the HCPs interviewed in phase 3 (30–45 years old; two countries) communicated digitally with patients through electronic medical records or other platforms, but none had used emojis with patients. HCPs agreed with the four scenarios identified by patients in phase 2, further suggesting that emojis may be helpful for patients with poor literacy or difficulty expressing emotions.ConclusionIn this study, patients and HCPs agreed that emojis could potentially enhance patient–HCP communication by facilitating emotional expression. Further research is required to evaluate the practicalities and benefits of integrating emojis into healthcare communications.