Emoji have grown to become one of the most important forms of communication on the web. With its widespread use, measuring the similarity of emoji has become an important problem for contemporary text processing since it lies at the heart of sentiment analysis, search, and interface design tasks. This paper presents a comprehensive analysis of the semantic similarity of emoji through embedding models that are learned over machine-readable emoji meanings in the EmojiNet knowledge base. Using emoji descriptions, emoji sense labels and emoji sense definitions, and with different training corpora obtained from Twitter and Google News, we develop and test multiple embedding models to measure emoji similarity. To evaluate our work, we create a new dataset called EmoSim508, which assigns human-annotated semantic similarity scores to a set of 508 carefully selected emoji pairs. After validation with EmoSim508, we present a real-world use-case of our emoji embedding models using a sentiment analysis task and show that our models outperform the previous best-performing emoji embedding model on this task. The EmoSim508 dataset and our emoji embedding models are publicly released with this paper and can be downloaded from http://emojinet.knoesis.org/.
Link to Paper - http://knoesis.org/node/2834
Link to Project Page - http://wiki.knoesis.org/index.php/EmojiNet
This paper presents the release of EmojiNet, the largest machine-readable emoji sense inventory that links Unicode emoji representations to their English meanings extracted from the Web. EmojiNet is a dataset consisting of: (i) 12,904 sense labels over 2,389 emoji, which were extracted from the web and linked to machine-readable sense definitions seen in BabelNet; (ii) context words associated with each emoji sense, which are inferred through word embedding models trained over Google News corpus and a Twitter message corpus for each emoji sense definition; and (iii) recognizing discrepancies in the presentation of emoji on different platforms, specification of the most likely platform-based emoji sense for a selected set of emoji. The dataset is hosted as an open service with a REST API and is available at http://emojinet.knoesis.org/. The development of this dataset, evaluation of its quality, and its applications including emoji sense disambiguation and emoji sense similarity are discussed.
Link to paper - http://knoesis.org/sites/default/files/ICWSM_2017_EmojiNet_Final_Wijeratne.pdf
Emoji’s sentiment score estimation using convolutional neural network with mu...IJECEIAES
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.
The ability to automatically process, derive meaning, and interpret text fused with emoji will be essential as society embraces emoji as a standard form of online communication. Yet the pictorial nature of emoji, the fact that (the same) emoji may be used in different contexts to express different meanings, and that emoji are used in different cultures over the world who interpret emoji differently, make it especially difficult to apply traditional Natural Language Processing (NLP) techniques to analyze them. This talk presents the creation of EmojiNet, the first machine-readable emoji sense repository that is designed by extracting emoji meanings from reliable online web sources and its applications for understanding emoji meaning in the social media text. It discusses how EmojiNet enables using NLP techniques to solve novel emoji research problems including emoji similarity and emoji sense disambiguation. A live demo of EmojiNet is available at http://emojinet.knoesis.org
The ability to automatically process and interpret text fused with emoji will be essential as society embraces emoji as a standard form of online communication. Since their introduction in the late 1990's, emoji have been widely used to enhance the sentiment, emotion, and sarcasm expressed in social media messages. They are equally popular across many social media sites including Facebook, Instagram, and Twitter. Processing emoji using traditional Natural Language Processing (NLP) techniques is a challenging task due to the pictorial nature of emoji and the fact that (the same) emoji may be used in different contexts and cultures to express different meanings. Their polysemous nature complicates tasks such as emoji similarity calculation and emoji sense disambiguation. Having access to machine-readable sense repositories that are specifically designed to capture emoji meaning can play a vital role in representing, contextually disambiguating, and converting pictorial forms of emoji into text, enabling NLP techniques to process this new medium of communication.
This dissertation presents EmojiNet, the largest machine-readable emoji sense inventory that links Unicode emoji representations to English meanings extracted from reliable online web sources. EmojiNet consists of: (i) 12,904 sense labels over 2,389 emoji linked to machine-readable sense definitions seen in BabelNet; (ii) context words associated with emoji senses based on word embedding models; and (iii) for some emoji, discrepancies in their presentation on different platforms. It further presents methods for emoji similarity evaluation and sense disambiguation uniquely enabled by EmojiNet. Emoji similarity methods are formed using word embedding models and are evaluated over a number of corpora. Those same embedding models are further used to carry out accuracy of emoji sense disambiguation. The EmojiNet framework, its RESTful web service, and benchmark datasets created as part of this dissertation are publicly released at http://emojinet.knoesis.org/.
Relevant publications: http://knoesis.org/Library?f%5Bsearch%5D=Sanjaya
In this lecture, we will look at why emoji are important and the reasons behind their increase in popularity, how emoji meanings are generated/assigned, how to calculate emoji similarity, and how to disambiguate emoji meanings.
This paper presents the release of EmojiNet, the largest machine-readable emoji sense inventory that links Unicode emoji representations to their English meanings extracted from the Web. EmojiNet is a dataset consisting of: (i) 12,904 sense labels over 2,389 emoji, which were extracted from the web and linked to machine-readable sense definitions seen in BabelNet; (ii) context words associated with each emoji sense, which are inferred through word embedding models trained over Google News corpus and a Twitter message corpus for each emoji sense definition; and (iii) recognizing discrepancies in the presentation of emoji on different platforms, specification of the most likely platform-based emoji sense for a selected set of emoji. The dataset is hosted as an open service with a REST API and is available at http://emojinet.knoesis.org/. The development of this dataset, evaluation of its quality, and its applications including emoji sense disambiguation and emoji sense similarity are discussed.
Link to paper - http://knoesis.org/sites/default/files/ICWSM_2017_EmojiNet_Final_Wijeratne.pdf
Emoji’s sentiment score estimation using convolutional neural network with mu...IJECEIAES
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.
The ability to automatically process, derive meaning, and interpret text fused with emoji will be essential as society embraces emoji as a standard form of online communication. Yet the pictorial nature of emoji, the fact that (the same) emoji may be used in different contexts to express different meanings, and that emoji are used in different cultures over the world who interpret emoji differently, make it especially difficult to apply traditional Natural Language Processing (NLP) techniques to analyze them. This talk presents the creation of EmojiNet, the first machine-readable emoji sense repository that is designed by extracting emoji meanings from reliable online web sources and its applications for understanding emoji meaning in the social media text. It discusses how EmojiNet enables using NLP techniques to solve novel emoji research problems including emoji similarity and emoji sense disambiguation. A live demo of EmojiNet is available at http://emojinet.knoesis.org
The ability to automatically process and interpret text fused with emoji will be essential as society embraces emoji as a standard form of online communication. Since their introduction in the late 1990's, emoji have been widely used to enhance the sentiment, emotion, and sarcasm expressed in social media messages. They are equally popular across many social media sites including Facebook, Instagram, and Twitter. Processing emoji using traditional Natural Language Processing (NLP) techniques is a challenging task due to the pictorial nature of emoji and the fact that (the same) emoji may be used in different contexts and cultures to express different meanings. Their polysemous nature complicates tasks such as emoji similarity calculation and emoji sense disambiguation. Having access to machine-readable sense repositories that are specifically designed to capture emoji meaning can play a vital role in representing, contextually disambiguating, and converting pictorial forms of emoji into text, enabling NLP techniques to process this new medium of communication.
This dissertation presents EmojiNet, the largest machine-readable emoji sense inventory that links Unicode emoji representations to English meanings extracted from reliable online web sources. EmojiNet consists of: (i) 12,904 sense labels over 2,389 emoji linked to machine-readable sense definitions seen in BabelNet; (ii) context words associated with emoji senses based on word embedding models; and (iii) for some emoji, discrepancies in their presentation on different platforms. It further presents methods for emoji similarity evaluation and sense disambiguation uniquely enabled by EmojiNet. Emoji similarity methods are formed using word embedding models and are evaluated over a number of corpora. Those same embedding models are further used to carry out accuracy of emoji sense disambiguation. The EmojiNet framework, its RESTful web service, and benchmark datasets created as part of this dissertation are publicly released at http://emojinet.knoesis.org/.
Relevant publications: http://knoesis.org/Library?f%5Bsearch%5D=Sanjaya
In this lecture, we will look at why emoji are important and the reasons behind their increase in popularity, how emoji meanings are generated/assigned, how to calculate emoji similarity, and how to disambiguate emoji meanings.
Extraction of Emoticons with Sentimental Barvivatechijri
The latest generation of emoticons which are called as emojis that is largely being used in mobile
communications as well as in social media. In past few years, more than ten billion emojis were used on Twitter.
Emojis which are known as the Unicode graphic symbols, which are basically used as shorthand to express the
concepts and ideas of the people. For smaller number of well-known emoticons, their meanings or sentiments
are well known but there are thousands of emojis so extracting their sentiments is difficult. The Emoji Sentiment
Ranking method which is used to evaluate a sentiment mapping of emojis by using sentiment polarity such as
negative, neutral, or positive. The sentimental classification of tweets with and without emoticons are very much
different.Finally, the method also gives representation of sentiments and a better visualization in the form of a
sentimental Bar.
Deriving Conversational Insight by Learning Emoji Representation by Jeff Wein...Data Con LA
Abstract:- It is a rare occurrence to observe the rise of a new language amongst a population. It is an even more rare occurrence to observe the adoption of such a language on a global scale. Since the introduction of the emoji keyboard on iOS in 2011, the use of emojis in textual communication has steadily grown into a common vernacular on social media. As of April 2015, Instagram reported that nearly half of all text contained emojis and, in some countries, over 60% of texts contained emoji characters. For power users of social media as well as for marketers looking for audiences on these platforms, it is becoming increasingly imperative to capture emoji data and derive insight from its use; to better understand what intent or meaning the usage carries in the conversation. Jeff Weintraub, VP of Technology at theAmplify, a creative Brandtech Influencer Service and a subsidiary of You & Mr Jones, the World's First Brandtech Group, will briefly summarize the data science behind learning emoji representations and also present recent trends in emoji usage within the context of advertising and branded marketing campaigns on social media.
Twitter Sentiment Analysis Project Done using R.
In these Project we deal with the tweets database that are avaialble to us by the Twitter. We clean the tweets and break them out into tokens and than analysis each word using Bag of Word concept and than rate each word on the basis of the score wheter it is positive, negative and neutral.
We used Naive Baye's Classifier as our base.
Abstract
Background: Indonesia is an active Twitter user that is the largest ranked in the world.
Tweets written by Twitter users vary, from tweets containing positive to negative responses.
This agreement will be utilized by the parties concerned for evaluation.
Objective: On public comments there are emoticons and sarcasm which have an influence on
the process of sentiment analysis. Emoticons are considered to make it easier for someone to
express their feelings but not a few are also other opinion researchers, namely by ignoring
emoticons, the reason being that it can interfere with the sentiment analysis process, while
sarcasm is considered to be produced from the results of the sarcasm sentiment analysis in it.
Methods: The emoticon and no emoticon categories will be tested with the same testing data
using classification method are Naïve Bayes Classifier and Support Vector Machine. Sarcasm
data will be proposed using the Random Forest Classifier, Naïve Bayes Classifier and
Support Vector Machine method.
Results: The use of emoticon with sarcasm detection can increase the accuracy value in the
sentiment analysis process using Naïve Bayes Classifier method.
Conclusion: Based on the results, the amount of data greatly affects the value of accuracy.
The use of emoticons is excellent in the sentiment analysis process. The detection of superior
sarcasm only by using the Naïve Bayes Classifier method due to differences in the amount
of sarcasm data and not sarcasm in the research process
Sentimental analysis of audio based customer reviews without textual conversionIJECEIAES
The current trends or procedures followed in the customer relation management system (CRM) are based on reviews, mails, and other textual data, gathered in the form of feedback from the customers. Sentiment analysis algorithms are deployed in order to gain polarity results, which can be used to improve customer services. But with evolving technologies, lately reviews or feedbacks are being dominated by audio data. As per literature, the audio contents are being translated to text and sentiments are analyzed using natural processing language techniques. However, these approaches can be time consuming. The proposed work focuses on analyzing the sentiments on the audio data itself without any textual conversion. The basic sentiment analysis polarities are mostly termed as positive, negative, and natural. But the focus is to make use of basic emotions as the base of deciding the polarity. The proposed model uses deep neural network and features such as Mel frequency cepstral coefficients (MFCC), Chroma and Mel Spectrogram on audio-based reviews.
Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment AnalysisSHAILENDRA KUMAR SINGH
The rapid growth of internet facilities has increased the comments, posts, blogs, feedback, etc., on a
large scale on social networking sites. These social media data are available in an unstructured form,
which includes images, text, and videos. The processing of these data is difficult, but some sentiment
analysis, information retrieval, and recommender systems are used to process these unstructured data.
To extract the opinion and sentiment of internet users from their written social media text, a sentiment
analysis system is required to develop, which can work on both monolingual and bilingual phonetic text.
Therefore, a sentiment analysis (SA) system is developed, which performs well on different domain
datasets. The system performance is tested on four different datasets and achieved better accuracy of
3% on social media datasets, 1.5% on movie reviews, 1.35% on Amazon product reviews, and 4.56%
on large Amazon product reviews than the state-of-art techniques. Also, the stemmer (StemVerb) for
verbs of the English language is proposed, which improves the SA system’s performance.
https://www.researchgate.net/publication/350987101_Classification_of_Code-Mixed_Bilingual_Phonetic_Text_Using_Sentiment_Analysis
Overview of the EVALITA 2018 Italian Emoji Prediction (ITAMoji) Shared Task. Presented in Turin, December 2018: http://www.evalita.it/2018/workshop-program
Type and Category of Memes used on social media HennaAnsari
One of the strengths of the memes is that memers may conunent on any political, social, cultural, and religious issue in a humorous a. satirical manner. Moreover, memes have become very popular among users due to their humorous nature and short duration. R may have very strong effect on their perceptions and opinions about different personalities and issues. So, it is import. to explore the nature and type of contents of memes and their impact on perceptions a. opinions of the users.
RESEARCH OBJECTIVES • To explore the types/categories of memes. • To explore the way contents of memes are presented on social media. • To explore the impacts of contents of memes on ethical values of users. • To investigate the influence of memes on opinion of users regarding different issues and personalities. • To find out the use of memes for promotion of brands on social media.
RESEARCH QUESTIONS RQ1: What are the types/ categories of memes? RQ2: How contents of manes are presented on Social Media? RQ3: How contents of mem. are having an impact on ethical values of users? RQ4: How memes influence the opinion of users regarding different issues and personalities? RQ5: How memes are used in promotion of bran. on Social Media
Evaluating sentiment analysis and word embedding techniques on BrexitIAESIJAI
In this study, we investigate the effectiveness of pre-trained word embeddings for sentiment analysis on a real-world topic, namely Brexit. We compare the performance of several popular word embedding models such global vectors for word representation (GloVe), FastText, word to vec (word2vec), and embeddings from language models (ELMo) on a dataset of tweets related to Brexit and evaluate their ability to classify the sentiment of the tweets as positive, negative, or neutral. We find that pre-trained word embeddings provide useful features for sentiment analysis and can significantly improve the performance of machine learning models. We also discuss the challenges and limitations of applying these models to complex, real-world texts such as those related to Brexit.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
More Related Content
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Extraction of Emoticons with Sentimental Barvivatechijri
The latest generation of emoticons which are called as emojis that is largely being used in mobile
communications as well as in social media. In past few years, more than ten billion emojis were used on Twitter.
Emojis which are known as the Unicode graphic symbols, which are basically used as shorthand to express the
concepts and ideas of the people. For smaller number of well-known emoticons, their meanings or sentiments
are well known but there are thousands of emojis so extracting their sentiments is difficult. The Emoji Sentiment
Ranking method which is used to evaluate a sentiment mapping of emojis by using sentiment polarity such as
negative, neutral, or positive. The sentimental classification of tweets with and without emoticons are very much
different.Finally, the method also gives representation of sentiments and a better visualization in the form of a
sentimental Bar.
Deriving Conversational Insight by Learning Emoji Representation by Jeff Wein...Data Con LA
Abstract:- It is a rare occurrence to observe the rise of a new language amongst a population. It is an even more rare occurrence to observe the adoption of such a language on a global scale. Since the introduction of the emoji keyboard on iOS in 2011, the use of emojis in textual communication has steadily grown into a common vernacular on social media. As of April 2015, Instagram reported that nearly half of all text contained emojis and, in some countries, over 60% of texts contained emoji characters. For power users of social media as well as for marketers looking for audiences on these platforms, it is becoming increasingly imperative to capture emoji data and derive insight from its use; to better understand what intent or meaning the usage carries in the conversation. Jeff Weintraub, VP of Technology at theAmplify, a creative Brandtech Influencer Service and a subsidiary of You & Mr Jones, the World's First Brandtech Group, will briefly summarize the data science behind learning emoji representations and also present recent trends in emoji usage within the context of advertising and branded marketing campaigns on social media.
Twitter Sentiment Analysis Project Done using R.
In these Project we deal with the tweets database that are avaialble to us by the Twitter. We clean the tweets and break them out into tokens and than analysis each word using Bag of Word concept and than rate each word on the basis of the score wheter it is positive, negative and neutral.
We used Naive Baye's Classifier as our base.
Abstract
Background: Indonesia is an active Twitter user that is the largest ranked in the world.
Tweets written by Twitter users vary, from tweets containing positive to negative responses.
This agreement will be utilized by the parties concerned for evaluation.
Objective: On public comments there are emoticons and sarcasm which have an influence on
the process of sentiment analysis. Emoticons are considered to make it easier for someone to
express their feelings but not a few are also other opinion researchers, namely by ignoring
emoticons, the reason being that it can interfere with the sentiment analysis process, while
sarcasm is considered to be produced from the results of the sarcasm sentiment analysis in it.
Methods: The emoticon and no emoticon categories will be tested with the same testing data
using classification method are Naïve Bayes Classifier and Support Vector Machine. Sarcasm
data will be proposed using the Random Forest Classifier, Naïve Bayes Classifier and
Support Vector Machine method.
Results: The use of emoticon with sarcasm detection can increase the accuracy value in the
sentiment analysis process using Naïve Bayes Classifier method.
Conclusion: Based on the results, the amount of data greatly affects the value of accuracy.
The use of emoticons is excellent in the sentiment analysis process. The detection of superior
sarcasm only by using the Naïve Bayes Classifier method due to differences in the amount
of sarcasm data and not sarcasm in the research process
Sentimental analysis of audio based customer reviews without textual conversionIJECEIAES
The current trends or procedures followed in the customer relation management system (CRM) are based on reviews, mails, and other textual data, gathered in the form of feedback from the customers. Sentiment analysis algorithms are deployed in order to gain polarity results, which can be used to improve customer services. But with evolving technologies, lately reviews or feedbacks are being dominated by audio data. As per literature, the audio contents are being translated to text and sentiments are analyzed using natural processing language techniques. However, these approaches can be time consuming. The proposed work focuses on analyzing the sentiments on the audio data itself without any textual conversion. The basic sentiment analysis polarities are mostly termed as positive, negative, and natural. But the focus is to make use of basic emotions as the base of deciding the polarity. The proposed model uses deep neural network and features such as Mel frequency cepstral coefficients (MFCC), Chroma and Mel Spectrogram on audio-based reviews.
Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment AnalysisSHAILENDRA KUMAR SINGH
The rapid growth of internet facilities has increased the comments, posts, blogs, feedback, etc., on a
large scale on social networking sites. These social media data are available in an unstructured form,
which includes images, text, and videos. The processing of these data is difficult, but some sentiment
analysis, information retrieval, and recommender systems are used to process these unstructured data.
To extract the opinion and sentiment of internet users from their written social media text, a sentiment
analysis system is required to develop, which can work on both monolingual and bilingual phonetic text.
Therefore, a sentiment analysis (SA) system is developed, which performs well on different domain
datasets. The system performance is tested on four different datasets and achieved better accuracy of
3% on social media datasets, 1.5% on movie reviews, 1.35% on Amazon product reviews, and 4.56%
on large Amazon product reviews than the state-of-art techniques. Also, the stemmer (StemVerb) for
verbs of the English language is proposed, which improves the SA system’s performance.
https://www.researchgate.net/publication/350987101_Classification_of_Code-Mixed_Bilingual_Phonetic_Text_Using_Sentiment_Analysis
Overview of the EVALITA 2018 Italian Emoji Prediction (ITAMoji) Shared Task. Presented in Turin, December 2018: http://www.evalita.it/2018/workshop-program
Type and Category of Memes used on social media HennaAnsari
One of the strengths of the memes is that memers may conunent on any political, social, cultural, and religious issue in a humorous a. satirical manner. Moreover, memes have become very popular among users due to their humorous nature and short duration. R may have very strong effect on their perceptions and opinions about different personalities and issues. So, it is import. to explore the nature and type of contents of memes and their impact on perceptions a. opinions of the users.
RESEARCH OBJECTIVES • To explore the types/categories of memes. • To explore the way contents of memes are presented on social media. • To explore the impacts of contents of memes on ethical values of users. • To investigate the influence of memes on opinion of users regarding different issues and personalities. • To find out the use of memes for promotion of brands on social media.
RESEARCH QUESTIONS RQ1: What are the types/ categories of memes? RQ2: How contents of manes are presented on Social Media? RQ3: How contents of mem. are having an impact on ethical values of users? RQ4: How memes influence the opinion of users regarding different issues and personalities? RQ5: How memes are used in promotion of bran. on Social Media
Evaluating sentiment analysis and word embedding techniques on BrexitIAESIJAI
In this study, we investigate the effectiveness of pre-trained word embeddings for sentiment analysis on a real-world topic, namely Brexit. We compare the performance of several popular word embedding models such global vectors for word representation (GloVe), FastText, word to vec (word2vec), and embeddings from language models (ELMo) on a dataset of tweets related to Brexit and evaluate their ability to classify the sentiment of the tweets as positive, negative, or neutral. We find that pre-trained word embeddings provide useful features for sentiment analysis and can significantly improve the performance of machine learning models. We also discuss the challenges and limitations of applying these models to complex, real-world texts such as those related to Brexit.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
1. A Semantics-Based Measure of
Emoji Similarity
Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)
Wright State University, Dayton, OH, USA
Presented at the 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI’17)
Leipzig, Germany, 23rd – 26th August, 2017.
Lakshika Balasuriya
lakshika@knoesis.org
Sanjaya Wijeratne
sanjaya@knoesis.org
Derek Doran
derek@knoesis.org
Amit Sheth
amit@knoesis.org
2. 1Appboy Blog – https://www.appboy.com/blog/emojis-used-in-777-more-campaigns/
2Wijeratne, Sanjaya et al. A Semantics-Based Measure of Emoji Similarity, Web Intelligence 2017.
Businesses extensively use emoji in consumer applications
Dominos App support ordering pizza by texting a pizza emoji
Recent research shows emoji can improve sentiment, emotion
and sarcasm detection (MIT’s DeepMoji)
3. 3
Why Emoji Similarity?
Measuring the Emoji Similarity can lead to
improvements in numerous tasks including;
Sentiment Analysis – Emoji features improve sentiment
analysis
Emoji-based Search – Similar emoji can improve recall
Designing of optimized emoji keyboards – To provide
intuitive emoji groups to show ~2,700 emoji on small-sized
screens
Suggesting alternative emoji domains – If an emoji domain
is already taken
Wijeratne, Sanjaya et al. A Semantics-Based Measure of Emoji Similarity, Web Intelligence 2017.
4. 4
Emoji Similarity Problem
Wijeratne, Sanjaya et al. A Semantics-Based Measure of Emoji Similarity, Web Intelligence 2017.
The notion of the similarity of two emoji is very broad
Pixel-based emoji similarity – Would not work as different platforms use
different emoji pictures to represent the same emoji (Miller et al., 2016)
Can we devise a notion of emoji similarity that
reflects the meaning or intended use of the emoji?
5. 5
Related Research
EmoTwi50 by Barbieri et al.: Used Word2Vec to learn
distributional semantics of emoji usage to calculate emoji
similarity
emoji2vec – Eisner et al.: Used emoji keywords (4 words on
average) from Unicode Consortium website and converted them
to emoji vectors using distributional semantics of words learned
via Word2Vec
Phol et al.
Used Word2Vec to learn distributional semantics of emoji
Used Jaccard Similarity on emoji keywords obtained from the
Unicode Consortium (Similar to Wijeratne et al. ICWSM 2017)
Wijeratne, Sanjaya et al. A Semantics-Based Measure of Emoji Similarity, Web Intelligence 2017.
6. 6
Our Emoji Embedding Approach
Wijeratne, Sanjaya et al. A Semantics-Based Measure of Emoji Similarity, Web Intelligence 2017.
Extract emoji definitions from a knowledge base of
emoji senses and definitions (EmojiNet)
Learn distributional semantics of words as word
embeddings using two corpora (Tweets and Google
News)
Convert the words in emoji meanings to vectors using
word embeddings (emoji embeddings)
Evaluate the similarity (distance) of emoji in the
embedding space using EmoSim508, a new dataset
with 508 emoji pairs
7. 7
Building EmojiNet
Wijeratne, Sanjaya et al. A Semantics-Based Measure of Emoji Similarity, Web Intelligence 2017.
The Unicode Consortium maintains a complete list of the standardized Unicodes for each
emoji along with other information such as manually curated keywords and images.
Emojipedia is a human-created emoji reference website that provides useful emoji
information such as emoji descriptions and short codes.
The Emoji Dictionary is the first crowdsourced emoji reference website that provides
emoji definitions with their sense labels based on how emoji are used in sentences.
BabelNet is the most comprehensive multilingual machine-readable semantic network
available to date that links words into their corresponding Wikipedia pages.
9. 9
Representing Emoji using Definitions
Wijeratne, Sanjaya et al. A Semantics-Based Measure of Emoji Similarity, Web Intelligence 2017.
Emoji Description (Sense_Desc.)
Gives an overview of what is depicted in an emoji – E.g., “A gun emoji,
more precisely a pistol. A weapon that has potential to cause great harm.
Displayed facing right-to-left on all platforms”
Emoji Sense Labels (Sense_Label)
Word-POS tag pairs (such as laugh(noun)). For gun emoji, EmojiNet lists
6 nouns (gun, weapon, pistol, violence, revolver, handgun), 3 verbs
(shoot, gun, pistol) and 3 adjectives (deadly, violent, deathly)
Emoji Sense Definitions (Sense_Def.)
Textual descriptions that explain each sense label and how those sense
labels should be used in a sentence
Combination of all of the above (Sense_All)
10. 10
Learning Emoji Embeddings
Wijeratne, Sanjaya et al. A Semantics-Based Measure of Emoji Similarity, Web Intelligence 2017.
Figure 2 – Learning Emoji Embedding Models using Word Vectors
11. 11
Ground Truth Data Creation
Wijeratne, Sanjaya et al. A Semantics-Based Measure of Emoji Similarity, Web Intelligence 2017.
110M Tweets were used to identify 508 most frequently co-
occurred emoji pairs, which covered 25% of the dataset. The full
emoji list is at –http://emojinet.knoesis.org/emojipairs508.htm
Figure 3 – Emoji Co-Occurrence Frequency Graph
12. 12
Ground Truth Data Creation Cont.
Wijeratne, Sanjaya et al. A Semantics-Based Measure of Emoji Similarity, Web Intelligence 2017.
Given an emoji pair, we asked 10 human annotators to
evaluate them for equivalence (Q1) and relatedness (Q2)
Q1 – Can the use of one emoji be replaced by the other?
Q2 – Can one use either emoji in the same context?
Lickert scale from 0 to 4 was used to rate equivalence and
relatedness
We averaged the equivalence and relatedness values for each
emoji to calculate the final similarity value for the two emoji –
http://emojinet.knoesis.org/emojipairs508_userstudy.htm
Krippendor’s alpha coefficient was used to calculate the inter
annotator agreement (α for Q1 = 0.632 and α for Q2 = 0.567)
13. 13
Intrinsic Evaluation
Wijeratne, Sanjaya et al. A Semantics-Based Measure of Emoji Similarity, Web Intelligence 2017.
Using four different emoji definitions (Sense_Desc.,
Sense_Label, Sense_Def., Sense_All) and two corpora
(Twitter and Google News), we trained eight emoji
embedding models for each emoji
We calculated emoji similarity of the 508 emoji pairs
using each embedding model
Using Spearman’s Rank Correlation Coefficient
(Spearman’s ρ), we compared the similarity rankings of
each model with ground truth data
14. 14
Intrinsic Evaluation Cont.
Wijeratne, Sanjaya et al. A Semantics-Based Measure of Emoji Similarity, Web Intelligence 2017.
Except for Sense_Desc.-based embedding models which
correlated moderately with ground truth data (40.0 < ρ < 59.0), all
other models show a strong correlation (60.0 < ρ < 79.0)
Sense_Labels-based embedding models correlate best with
ground truth data
Table 1 – Spearman’s Rank Correlation Results
15. 15
Extrinsic Evaluation
Wijeratne, Sanjaya et al. A Semantics-Based Measure of Emoji Similarity, Web Intelligence 2017.
We tested our emoji embedding models using a
sentiment analysis baseline
Our baseline had 12,920 English tweets, and 2,295 of them
had emoji
All words in the tweets were replaced with their
corresponding word embeddings and emoji were replaced
with emoji embeddings learned
Emoji embeddings based on Sense-labels outperformed
previous best performing model
16. 16
Extrinsic Evaluation Cont.
Wijeratne, Sanjaya et al. A Semantics-Based Measure of Emoji Similarity, Web Intelligence 2017.
Table 2 – Accuracy of the Sentiment Analysis task using Emoji Embeddings
17. 17
Key Takeaways
Wijeratne, Sanjaya et al. A Semantics-Based Measure of Emoji Similarity, Web Intelligence 2017.
We created a dataset that can be used to evaluate emoji
similarity tasks – Download link http://emojinet.knoesis.org/
Combining emoji sense knowledge with distributional semantics
could improve the emoji embedding models
Longer sense definitions are not suitable to learn emoji
embeddings
Longer definitions contain words that do not directly contribute to the
meaning of an emoji, thus, add noise to the learned embedding models.
Sense labels, which are directly related to the meaning of emoji tend to
result in better embedding models
18. 18
Future Work
Wijeratne, Sanjaya et al. A Semantics-Based Measure of Emoji Similarity, Web Intelligence 2017.
Extend emoji embeddings to capture the differences in
emoji interpretations due to how they appear across
different platforms or devices
Use emoji embedding models in other downstream
applications such as emoji-based search
19. 19
Acknowledgement
We are thankful to the annotators who helped us in creating the EmoSim508 dataset.
We acknowledge partial support from the National Institute on Drug Abuse (NIDA)
Grant No. 5R01DA039454- 03: “Trending: Social Media Analysis to Monitor Cannabis
and Synthetic Cannabinoid Use”, and the National Science Foundation (NSF) award:
CNS-1513721: “Context-Aware Harassment Detection on Social Media”. Points of view
or opinions in this document are those of the authors and do not necessarily represent
the official position or policies of the NIDA or NSF.
Wijeratne, Sanjaya et al. A Semantics-Based Measure of Emoji Similarity, Web Intelligence 2017.
20. 20
Related Publications
Sanjaya Wijeratne et al. A Semantics-Based Measure of Emoji
Similarity (Web Intelligence 2017)
Sanjaya Wijeratne et al. EmojiNet: An Open Service and API for
Emoji Sense Discovery (ICWSM 2017)
Sanjaya Wijeratne et al. EmojiNet: Building a Machine Readable
Sense Inventory for Emoji (SocInfo 2016)
Wijeratne, Sanjaya et al. A Semantics-Based Measure of Emoji Similarity, Web Intelligence 2017.
21. 21SML @ IJCAI 2016 Wijeratne, Sanjaya et al. Word Embeddings to Enhance Twitter Gang Member Profile Identificationamit@knoesis.org http://knoesis.org/amit
Thank You!
Visit us at http://emojinet.knoesis.org/
Editor's Notes
Sentiment Analysis – It has been shown that using emoji as features can improve sentiment analysis, so similar emoji can further strengthen sentiment analysis results.
Emoji-based Search – Similar words can improve the recall of information retrieval and search. Similarly, similar emoji can help improving the recall for emoji-based search.
Optimized Keyboard Design – Currently, there are close to 2,700 emoji supported by the Unicode Consortium and none of the emoji keyboards can show the full list of emoji in small display screens. Emoji similarity can be used to group emoji based on their similarity, which could lead to optimized emoji keyboard designs.
Emoji domain name suggestions – Emoji domain names are getting popular now and emoji similarity can be used to suggest alternate emoji domain names if a particular domain name is already taken.
We are interested in measuring the semantic similarity of emoji such that the measure reflects the likeness of their meaning, interpretation or intended use.
EmoTwi50 by Barbieri et al. – They used a collection of tweets to train a word2vec model which they then used to learn the distributional semantics of emoji.
emoji2vec – Eisner et al. – They represented an emoji using the keywords listed in the Unicode Consortium Webpage. Then they replaced each keyword listed for emoji using their corresponding word embeddings learned by the Google News corpus and summed the word embeddings for all keywords belong to a given emoji. They used the resulting embedding model as the emoji embedding for a given emoji.
Phol et al. – They learned distributional semantics of emoji the same way Barbieri et al. learned using Word2Vec. They also calculated Jaccard Similarity of two emoji using the overlap between the emoji keywords listed in the Unicode Consortium website. This is similar to our previous work which we published in ICWSM 2017 (Link - http://knoesis.org/people/sanjayaw/papers/2017/ICWSM_2017_EmojiNet_Final_Wijeratne.pdf).
Our approach is based on representing emoji definitions available in a machine-readable emoji sense inventory using distributional semantic models to learned over a large corpus of text. This is the first effort that uses a machine-readable sense inventory with distributional semantics models.
We use Word2Vec model to first learn word vectors using a large corpus of text. Then for each word in each emoji definition, we replace the word with its corresponding word vector. Then we take the vector average of all vectors that belongs to words in an emoji difinition.
We didn’t want to hand pick emoji pairs as past studies have done. At the same time, we didn’t want to randomly select emoji pairs too, as it is shown that the random emoji pairs are not meaningful in majority of the cases. Thus, we wanted to come up with a way to get a meaningful set of emoji pairs without hand picking them. To do that, we followed the below approach.
We first collected 147 million tweets by using emoji as query terms. Then we removed retweets, which gave us a sample of 110 million unique tweets. Then, we created a graph where we plot the most frequently co-occurring emoji pairs against the co-occurring frequency. We selected the 508 most co-occurring emoji pairs as those 508 emoji pairs alone covers 25% of the whole dataset (110 million).