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Tag⁽⁴⁾Emojiall News⁽⁴⁾2021⁽¹⁾Updates⁽¹⁾Emoji Tag Cloud⁽¹⁾Twitter⁽¹⁾TF-IDF⁽¹⁾

Year⁽⁴⁾2021⁽⁴⁾

🔍Emoji Sentiment Analysis

Visual charts for emoji sentiment analysisWhat is Sentiment Analysis?The usage and prospect of Emoji Sentiment AnalysisRelated LinksApplication of sentiment analysis in emojiThe process of Emoji Sentiment AnalysisEmoji are full of emotion. It can enhance or adjust the tone and mood of the text, at the same time, the emotions expressed by emoji can be more obvious than words only. For example this emoji👿(angry face with horns). It is in the form of a devil character obviously, so there may have thoughts considered it is a "bad" emoji, like some evil thoughts in someone's head. But still, this kind of description is very abstract. Therefore, can we visualize the sentiment types and levels expressed by these emojis? The answer is "Yes" of course! We performed Natural Language Processing (NLP) on public samples of no less than 50 million tweets, and we use Text Sentiment Analysis (also called opinion mining) to associate the emoji sentiment with a set of values and visualize it. We spent a lot of time, energy and computing power to make our emoji sentiment analysis more scientific and rigorous, so that everyone can feel the charm and get better understanding of emoji.Data such as emoji sentiment value were calculated according to text sentiment analysis, which belongs to statistics and probability theory. After we solved the problem of algorithm and computing power, then encountered new difficulties: how to make users understand these professional data? A thought crosses our mind—— Hey! How about we visualize emoji sentiment values with some cool designs! 💡: We converted the calculated and analyzed data (left) into a chart that can be better understood by users (right), which can more intuitively show the emotional division of emoji; So let me explain the following chart, which depicts the results of emoji's sentiment tendency analysis in actual communication. 💡: 2 types of charts for emoji 👿's sentiment analysis, here we only analyze the chart above. The semicircle arc in the picture is divided into three colors, representing different emotional tendencies of emoji 👿. As we can see, the proportions of these three emotions are about 4:1:5, green and orange have similar proportions, which means this emoji is more a neutral one (surprise!!). The gray cursor is the Confidence Level, a statistical concept. Its position and width indicate: Expected Value ± Confidence. Expected Value: It is the weighted average of dispersive random variables of emoji sentiment value, used to express probability, c ∈ [−1， 0， +1]. Confidence: It is the parameter set of emoji sentiment value distribution, shows a reasonable error range in the form of positive and negative interval. And larger the number of corpus samples are, the smaller the error. Simply speaking, the closer cursor is to the left, the emotion of this emoji is more negative. On the contrary, the closer cursor is to the right, the more positive emotion expressed by this emoji. And the narrower the cursor is, the judgment of the emotion is more accurate. Now, can you understand our chart of emoji sentiment analysis?I believe that you have understood the data chart of emoji sentiment analysis, so let's talk about what is sentiment analysis. Sentiment analysis is also called opinion mining. Technically speaking, sentiment analysis is part of natural language processing (NLP) research. Sentiment analysis methods are divided into two types: Machine learning and Dictionary-based methods. But with the development of deep learning technology, the use of this technology for sentiment analysis has become the mainstream. The sentiment analysis process includes data preprocessing, feature engineering and model training. Generally speaking, the data preprocessing stage mainly divides the text into words and removes stop words and punctuation. However, our sentiment analysis retains punctuation marks and stop words. In the feature engineering stage, we chose the word embedding representation (Word2Vec) proposed by the Google team in 2013, which is divided into the CBOW (continuous bag of words) model and the Skip-gram model. The model results are as follows: 💡：On the left is the CBOW model, on the right is the Skip-gram model CBOW is based on the context to predict the target word to train to obtain the word vector. As shown in the figure, W(t) is predicted based on four words W(t-2), W(t-1), W(t+1), W(t+2); And skip-gram is to predict the surrounding words according to the target word to train to obtain the word vector. As shown in the figure is to predict W(t-2),W(t-1),W(t+1),W(t+2) according to W(t). In the model prediction stage, we divide the data into two parts: The training set divided at a ratio of 5:1 and the test set and the training set are processed out of order.Sentiment analysis is widely used in marketing, advertising, psychology, medical and other fields. We decide to do emoji sentiment analysis is to help people get deeper understanding of emoji in actual social interaction, eliminate ambiguity and misunderstanding of emoji more effectively. For example, when you are chatting with your friend (or your crush) online, the other person may sometimes send you emojis that you don't quite understand. Actually, there are many situations like this, such as: What does 🤒 mean if someone replies to my photo? What does it mean when a girl sends 😊 to me? If my crush sends me ❤, is that a good sign? As we know, you cannot tell an emoji's accurate meaning, however, through our sentiment analysis, you may can analyze these emojis psychologically, like "she sent me a 💞, it is a positive sign, maybe I should ask her out" or "why my boyfriend sends me a 😒? It is kind of negative, is he mad at me?", something like this. With our Emoji Sentiment Analysis, I believe you can understand emoji more deeply. All in all, we have paid a lot for this emoji sentiment analysis project. Even though it is difficult, we still want to make it deeper. We are now analyzing emoji in different languages, the emotional gain effect of emoji on pure text, and several other advanced studies have also been considered. All these are to explore more usage of emoji, and hope you find emoji are interesting and informative. Emoji Sentiment Analysis Sentiment analysis is a comprehensive analysis method that combines deep learning and statistics. We have obtained the sentiment value of emoji after complex analysis and calculation of reams of data, and the complete emoji sentiment analysis process is as follows: Label social networking corpus Data preprocessing Divide the dataset: Training Set(80%), Testing Set(20%) Use LSTM neural network to build a model According to the performance of the model on the test set, adjust the hyperparameters to improve the generalization ability of the model Perform the same data preprocessing action on unlabeled data Use the trained sentiment prediction model to predict the sentiment tendency on unlabeled data We perform sentiment analysis on emoji, and use deep learning technology to train to get an emoji text sentiment classifier. For the output layer of the classifier, we choose the sigmoid function to activate, and project the output layer results into the interval of 0-1. The closer the text is to 0, the more negative it is, and the closer it is to 1, the more positive it is. The sigmoid function formula is: F(x)=1/(1+e-x) We use a large sample data of 50 million tweets containing emoji as an analysis corpus, then put the corpus need to be analyzed into the trained sentiment classifier for sentiment prediction. Finally, the results of sentiment prediction by the classifier are divided into three types: negative, neutral and positive. The classification criteria are: Anyway, it is difficult, but we made it!!

2021-06-24 14:55:45 9

🕸Emoji Relationship Graph: Another extended research of emoji!

What is Emoji Relationship Graph？How to understand the Emoji Relationship Graph?How to calculate the relationship between emoji?ConclusionNowadays more and more practical and interesting emoji usages are created, for example, different categories of emoji will be combined together to become a Meme, or seemingly unrelated emoji can actually be used to describe the same meaning, which makes emoji gradually create a connection between them. We want to show this relationship between emoji visually and let users have a deeper understanding of emoji usage, so we use machine learning algorithms to calculate the relationship between emoji and draw the result into an easy-to-understand picture, which is the Emoji Relationship Graph. It is well known that emojis are usually presented as images, but it is impossible to figure out the relationship between emoji with images, because the main use of emoji is to convey information and emotions, they are more like a language, so it will be more accurate to explore their relationship through the meaning and usage of emoji. Therefore, we obtained all the tweets containing emoji from 2018 to 2021, totaling 812 million tweets. Considering that the usage of emoji will be different in different language environments, we classified the tweets by language, and then calculated the Text Similarity between emoji in each language by machine learning algorithm, so as to get the corresponding Emoji Relationship Graph for each language. This is the Emoji Relationship Graph of 👉 for spanish. The red box shows the 9 nearest emoji to it, the length of the ray represents the degree of relationship, the shorter the ray, the closer the relationship. In addition, the graph also shows a part of other emoji's relationship graph. In the black box is the relationship graph of 👆，and in the orange box is the relationship graph of ⤵. The results reflected in the relationship graph allow us to have a richer understanding of the usage of emoji. For example, these two emojis 👈🥺 are often used as a combination with 👉 in recent years to express aggrieved, shy or pleading, so they appear in this relationship graph: These two emojis are often used to indicate links, which means they have similarities in usage, so they are close to each other in the relationship graph: If you delve into the emoji relationship chart, you may refresh your perception of some emojis. Next we will give you a detailed description of the calculation process. It can be roughly divided into the following three steps: First, we use the TF-IDF algorithm to extract the tags of each emoji from the tweets and the weight corresponding to each tag. Tags refer to those words that are most closely related to emoji, which are equivalent to the characteristics of an emoji; and weights refer to the closeness of the relationship between tags and emoji, the higher the weight, the closer the relationship. About the algorithm and calculation process of getting tags, we have written an article to introduce it in detail, you can click the right link to read it: ☁️Emoji Tag Cloud: Help You To Get More Knowledge Of Emoji! After obtaining the labels, a new problem arises. When it comes to calculations, we all know that generally only numerical values can be calculated, but the tags are presented as text, so how can they be calculated algorithmically? Therefore, our second step is to convert the text into the numerical values that can be calculated——Vector. This process is called Word Embedding. We need to first read a large amount of tweet data using the word2vec algorithm (one of the methods of word embedding)to transform each word in the text into a vector, then we can obtain a word embedding matrix consisting of all high-dimensional vectors of words, and later map each word corresponding to each tag we got in the first step into a high-dimensional vector through the word embedding matrix. Thus, we have completed the text-to-vector conversion. These high-dimensional vectors are calculated by analyzing the context of the text, which can well preserve the semantic information of each word and thus ensure the accuracy of the text similarity. The word2vec algorithm is also explained in detail in our blog, if you want more details you can read it: 🔍Emoji Sentiment Analysis The last step is to calculate the text similarity between emojis. The algorithm commonly used to calculate text similarity is VSM（Vector Space Model). This is one of the most widely used similarity calculation models, but it obtains results by calculating co-occurring words (words that appear in both texts) of two texts, which is not accurate when facing texts with the same meaning but different wording.So to avoid this situation, we chose another algorithm——SCM(Soft Cosine Measure). It can measure the similarity between words, so even if two texts do not have words in common, this algorithm can calculate the similarity of two texts by evaluating the word similarity. The final calculation results are presented in numbers, the larger the number we get means the higher the text similarity between emojis, and the higher the text similarity, the closer their relationship. Through the relationship graph, we can understand people's habits and preferences of using emoji, and explore the trend of emoji usages. You may be surprised to find that some emojis you wouldn't associate with each other are actually very closely related, and that may be a new trendy use of emoji you haven't known yet! Also, if you have any suggestions, please tell us in the comment!

2021-12-01 08:47:59 15

🗂Some Update And Emoji Analyzes That Summarized EmojiAll 2021

EmojiAll & Emoji Usage In Different CountriesEmoji of the year (EmojiAll .ver)About commentsOther contents we added and updated in 2021Recently, the Unicode Consortium released the 'Most Used Emoji Of 2021', and each website has started its own year-end summary. Following this trend, in the closure of 2021, here we try to do some related summaries about emoji and our emojiall.com under our own analyzing angle. Here should be mentioned that all these data are from our website internal statistics, google analytics and Twitter for reference only💡. Now that we are talking about rankings, here I want to share with you some of the latest website internal rankings related to emoji that you may interested in. It is a global ranking, just like the Unicode Consortium's recent blog. The most clicked emoji on EmojiAll🖱️ The most searched emoji on EmojiAll🔍 The most liked emoji on EmojiAll👍 The most potential emoji on EmojiAll✊ The most commented emoji on EmojiAll💬 We added comments and login functions in March, which further increased the interactivity between users. Whether it is text comments or emojiarts, we are all very grateful for netizens' enthusiastic participation. Here we collected some of the popular comments that we believed is quite funny. I personally divide them into three types: knowledge-share type, interactive type and emojiart type. Knowledge-share type: Parts of users like to introduce some popular emoji usages to other users in an emoji comment, or why this emoji is suddenly become wide used. Such as these 2 emojis below👇. Interactive type: Here is my favorite type of comment! Some users like to interact with the emoji in some specific emoji comments, which is quite cute. For example ⛩️, netizens treat it as an actually shrine and began to pray in the comments... 🔺Hope to become rich, hope to succeed in the postgraduate entrance examination, hope to become a beauty, and there are even people who want world peace👍 Emoji art type: Since we published 👨‍🎨Let's Make Some Emoji Art, more and more netizens prefer to create their own emoji art in the comments, which is very creative. 🔺We got Minecraft, Squid Game and Spider Man!! In addition, by analyzing the time distribution of user comments in the background, we can know that lots of users comment on our website during working hours👨‍💻. Among them, the number of people reached the peak around 3PM, and some users still visited our website in the early hours of the morning. All in all, thank you all for your support!!Except for the leaderboard and comment functions mentioned above, in 2021, we also added other new emoji analysis contents such as sentiment analysis, location analysis, relationship graph and tag cloud. Through these contents, we believe that you will gain more understanding of emoji and be able to use them more flexibly. At the end of the year, after several months of modification, the last major update we launched was the improvement of the search function. Now you can use advanced search on emojiall to find emojis, emoji memes, blog posts, emoji topic, etc. Anything you might be interested to know!! By the way, after we analyze the search results on the website backstage, we found out that the search terms of each country also have their own characteristics. Here we still make the top 5 visited languages as examples. 🔺Chinese top 5 words: Fire, melon, heart, flower, gift. 🔺Spanish top 5 words: Applause, heart, diamond, bisexual, arrow. 🔺Portuguese top 5 words: Arrow, book, heart, nursing, nutrition. 🔺English top 5 words: S*x, heart, moon, sun, arrow. 🔺Turkish top 5 words: Heart, draw, music, lion, chat.You may have noticed that all our ranking sometimes has some differences compared with Unicode's conclusion. It is of note here that our data sources are different💡: while Unicode's emoji rank is based on median frequency of use across multiple sources, EmojiAll uses our own website internal data and publicly available social media data. Over time, our data will become more and more accurate. In short, the end of the year is a good opportunity for self-summary, reviewing a year's works, check if anything is missing and close loopholes📝. All our efforts are to promote emoji to the world, reduce misunderstandings about the meaning and usage of emojis, and analyze emojis in many unique angles. We will continue to work hard and keep releasing funny contents in the future. We are looking forward to your continued attention!Since emojiall.com launched in 2019, we are now supporting 46 languages, and according to internal statistics, the top 15 most visited languages and their changing trends are shown below👇.As for the countries, here we also list the top 15 most visited countries and their trends over the months in case you are interested in.On this basis, we launched Popular Emoji Leaderboards based on multiple languages this year. This is one of the popular new contents we released in 2021. Using this leaderboard, we can know which emoji is most popular in a country, you can even filter time, language, gender, age or category in our ranking list. This means you can get lots of knowledges from our emoji ranking lists, such as a country's preference for emoji use, the ratio of emoji used between male and female in this country, etc. Here we take the top 5 of the above countries as examples. Based on our leaderboard, among these countries, the Top 5's most used emojis are like below. Brazil: 🛐✈︎❤️‍🔥☯︎🙄 China: 🤤🇨🇳😅❤🚬 Turkey: 🥰🤍🥺🤎✈︎ Iran: 🤌🖕∞🥺🗿 Indonesia: 🗿❤️‍🔥✔️😇☭ From the above ranking, we can infer that maybe Chinese people like to use their own flag emoji, while in Iran and Indonesia, people may like to use Unicode characters like [∞] or [☭], and they all prefer [🗿moai] maybe.

2021-12-28 11:51:41 8

☁️Emoji Tag Cloud: Help You To Get More Knowledge Of Emoji!

How we get tags?The professional technology explanation of tag extractionHow to use our Emoji Tag Cloud?Every emoji is special. As long as you analyze it in a right way, you can find out many information such as meanings, category, related topic, even some unique usage of an emoji at a glance👀. After we perform a great amount of calculation and analysis to every emoji, we finally get Emoji Tag Cloud, keywords and phrases related to a specific emoji, and represent it in a novelty visual way. 🔺The Tag Cloud of emoji [unicorn🦄]As we all know, Twitter is a global famous social networking service, and people like to tweet texts with emojis to express their emotions or just as decoration. Most of our tags is from tweets all over the world. We analyze tweets from 2018.01 to 2021.11 and extract the tags from 812 million of tweets that contain emojis. Then we use statistics and algorithms to pick out the texts that are highly relevant to a specific emoji, and distinguish the language. Throught this, we can even know how an emoji is used in different country. Here we use english site as an example. These 2 emojis in our english page: 😎(smiling face with sunglasses) and 🦄(unicorn). We perform tag extraction to them and get words like [cool] or [nicki]. Here shows up a new problem❓: Of course there are lots of texts related to an emoji, then how we choose the most suited tag, and how to arrange them? In this situation, they involve further algorithms.There are many types of tag extraction technologies with different effects, such as abstract extraction for articles, and keyword tagging algorithms for short texts. Our "Emoji Tag Cloud-Twitter Tag Extraction" is based on the TF-IDF algorithm. It carried out with a modified process based on the characteristics of the Twitter data, which is an unsupervised short text label extraction algorithm. To facilitate understanding, here we summarize the procedure of tag extracting into 3 steps. First, we perform emoji extraction and text cleaning for each tweet with a month as a unit, remove topics and nicknames like [@xx], [#xx], and delete the tweet URL. During the text cleaning, we also filter stop words in different languages (for example, remove some modal particles like ah, oh, etc.), abbreviations, word forms, capitalization and other factors, finally get word frequency information data corresponding to each emoji. Second, we use the TF-IDF (term frequency-inverse document frequency) text representation algorithm to calculate an initial label text weight based on the results obtained in the previous step. The calculation formula is: TF-IDF = TF * IDF TF (Term Frequency) is obtained by dividing the number of occurrences of a word corresponding to an emoji by the total number of words corresponding to the emoji. IDF is Inverse Document Frequency, IDF = log( N / N(w) ), while [N] represents the total number of emojis, [N(w)] represents the number of emojis containing the word [w]. 🔺When a word appears in both emojiA and emojiB, it means that the word is not representative enough, and the weight of this word should be reduced. From the formula IDF = log( N / N(w) ), it can be seen that the range of IDF between 0 and positive infinity decreases with the increase of N(w). When a term appears more times in an article, it means that the weight of the term is greater. However, the words that appear most often are words that express tone or have no actual meaning, such as [aww], [oh] or [RT]. It is difficult to avoid the filtering of such words if only sorting and filtering by the TF value. So the IDF inverse document number is introduced as a constraint, in order to calculate a more accurate value to represent the weight of the label text. At the end of step 2, we will filter the entries that appear more than 15% of the total number of emojis. The units calculated in the first two steps are monthly data, and the total data is four years. At the last step, we will perform another round of consolidated statistical calculations on all monthly data. Convert the calculated four-year tweet data into the form of [(sum(tfidf_m) / M) * log(M)], and further calculate the weight of each tag entry. [sum(tfidf_m)] represents the sum of the TF-IDF values of the term in each month, and [M] represents the number of months in which the term appears. These, then, is the approximate calculation method of emoji tag data. After the final data is summarized, we will also manually check and filter according to the language in order to get more accurate tag results. In addition, the tags will also use CLDR short name and CLDR keywords for reference, they are the most basic tag text, which means you will always see some of these words in the Emoji Tag Cloud. 🔺 When an emoji is submitted to Unicode Consortium, it is necessary to have CLDR short name and CLDR keywords in its proposal, so these words must take into tag choosing consideration. The information of emoji [unicorn🦄], we put its short name and some of keywords into its tag cloud.It been quite a time we released the Emoji Tag Cloud. Personally, I believe it is a very funny and useful tool to observe a specific emoji, sometimes you can even know which group or trending topic prefer to using this emoji. Let me show you how to use our Emoji Tag Cloud! As mentioned above, the weight of each tag text is different. You can simply judge the correlation between tags and emoji by the size of circle (the bigger, the more relevant). Or you can put your mouse on the circle, there will be a small square with [number, tag text] show up. In this situation, the smaller the number is, the tag in this circle is more relevant to the emoji. You can also click these tags to search other related emojis! Still, we use unicorn as example. the Tag Cloud of emoji [unicorn🦄] is like below: As you can see, the Top 5 tags of 🦄 are [unicorn], [nicki], [unicorns], [plt] and [barbz]. Word [unicorn] is the CLDR short name of this emoji, and the rest of 4 tags are all extracted from twitter. [nicki] and [barbz] are related to Nicki Minaj and her fan group, and word [plt] is refer to [Pretty Little Thing], a UK-based fashion retailer or just this phrase. 🦄 is a popular emoji in SNS, and a very representative emoji for Nicki Minaj fans. If you love Nicki, you definitely should use this emoji! All in all, by using our Emoji Tag Cloud, you can easily find out the related contents of a specific emoji, maybe you can even know more about pop culture and avoid embarrassment of not knowing the basic and extended meaning of an emoji. Sometimes the using of an emoji can become a social phenomenon, such as this emoji 🥺 in Japan. As an emoji to express begging, grievance or loveliness, this word 'ぴえん🥺' won the "JC&JK Buzzword Awards 2019'", "2019 Gal Buzzword Award" 2nd place, the first place of "Instagram Buzzword Award for the first half of 2020" and became a trend emoji all over Japan! In this case, for some people, Emoji Tag Cloud is also a great tool to get know about internet culture, all depend on how you use it. All these are to explore more usage of emoji, and hope you find emoji are interesting and informative. In order to provide you accurate emoji-related contents, the data will also keep updating. If you have any more advice about Emoji Tag Cloud, please let us know in the comments below👇!

2021-11-16 10:12:40 3