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Improving Emoji Understanding Tasks using EmojiNet – A Mini-Tutorial

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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

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Improving Emoji Understanding Tasks using EmojiNet – A Mini-Tutorial

  1. 1. Improving Emoji Understanding Tasks using EmojiNet – A Mini-Tutorial Presented at the 1st International Workshop on Emoji Understanding and Applications in Social Media (Emoji2018) Co-located with the 12th​ International AAAI Conference on Web and Social Media (ICWSM-18) Stanford, California, USA. 25th June, 2018 1Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Wright State University, USA Amit Sheth1 amit@knoesis.org Sanjaya Wijeratne1 sanjaya@knoesis.org Horacio Saggion2 horacio.saggion@upf.edu 2Department of Information and Communication Technologies, Universitat Pompeu Fabra, Spain
  2. 2. Emoji Use in Social Media & Email 2Image Source – https://goo.gl/ttxyP1 Image Source – https://goo.gl/n3vBtW Image Source – https://goo.gl/J5FYex
  3. 3. Emoji Usage on Social Media - Instagram 3Image Source – https://goo.gl/os8iGa %ofcommentsthatcontainemoji
  4. 4. Emoji are Taking Over Slang Terms 4 Image Source – https://goo.gl/os8iGa xoxo, omg, muah, babe, bae, lol, haha, hehe %ofcommentsthatcontainemoji
  5. 5. Emoji are Taking Over Emoticons 5Image Source – https://goo.gl/ZcMzB6
  6. 6. Can Computers Understand Emoji? 6 Can we devise methods to automatically process emoji in social media text so that a computer would be able to automatically interpret emoji ?
  7. 7. Emoji Understanding 7 Image Source – https://goo.gl/rjS1hX I Look
  8. 8. Emoji Understanding Cont. 8
  9. 9. Challenges in Emoji Understanding • Emoji were defined with no rigid semantics ̶ Emoji are inherently polysemous. The meaning of an emoji can change based on the language, culture, and context [Unicode.org] ̶ Differences in rendering emoji [Miller et al., 2016] 9 Gas Vs Marijuana Maple Leaf Vs Marijuana
  10. 10. Challenges in Emoji Understanding Cont. • Lack of resources for emoji understanding ̶ Multiple websites provide basic information on emoji • E.g., emoji names, pictures, descriptions ̶ No resource available that can be used to extract machine-readable emoji meanings • Can a machine-readable emoji meanings repository improve emoji understanding? 10
  11. 11. Tutorial Overview • Overview of EmojiNet ̶ We will look at the construction of a machine-readable emoji sense inventory called EmojiNet – [EmojiNet by Wijeratne et al.] • We will look review two emoji understanding tasks ̶ Emoji Similarity – [EmoTwi50 by Barbieri et al.], [emoji2vec by Eisner et al.], [Phol et al.], [EmoSim508 by Wijeratne et al.] ̶ Emoji Sense Disambiguation – [MojiSem by Na’aman et al.], [Donato et al.], [EmojiNet by Wijeratne et al.] ̶ We will examine how EmojiNet can help solve the above task 11
  12. 12. 1. EmojiNet: Building a Machine-readable Emoji Sense Inventory Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran. EmojiNet: An Open Service and API for Emoji Sense Discovery. In 11th International AAAI Conference on Web and Social Media (ICWSM 2017). Montreal, Canada; 2017. [Kno.e.sis Library Page] | [PDF] | [BibTeX] Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran. EmojiNet: Building a Machine Readable Sense Inventory for Emoji. In 8th International Conference on Social Informatics (SocInfo 2016). Bellevue, WA, USA; 2016. [Kno.e.sis Library Page] | [PDF] | [BibTeX] 12
  13. 13. Overview of Emoji Resources on the Web • There was no resource available that could provide machine-readable emoji meanings ̶ The Unicode Consortium – Used to list keywords associated with emoji meanings (e.g., is associated with face, thinking, and think keywords) ̶ Emojipedia – Contains emoji descriptions (e.g., - A face shown with a single finger and thumb resting on the chin, glancing upward. Used to indicate thinking, or deep thought.) 13 Image Source – http://www.unicode.org/emoji/charts/full-emoji-list.html
  14. 14. Overview of Emoji Resources on the Web Cont. ̶ Emoji Dictionary – Crowdsourced resource of emoji sense labels (e.g., laugh(Noun)) 14 Image Source – https://emojidictionary.emojifoundation.com/thinking_face
  15. 15. Challenges in Resource Integration • Different emoji resources on the web carry valuable information that can complement each other when they are combined ̶ Emoji Dictionary does not contain Unicode code points of emoji ̶ Emoji Dictionary emoji senses are not connected to machine-readable dictionaries ̶ Emoji Dictionary does not contain information about all emoji supported by the Unicode Consortium 15
  16. 16. What is EmojiNet? • The largest machine-readable emoji sense inventory ̶ Contains 12,904 emoji sense definitions over 2,389 emoji supported by the Unicode Consortium. Emoji sense definitions are linked to BabelNet machine-readable dictionary ̶ Context words are learned for each sense definition using two large text corpora to further strengthen the sense definitions ̶ Platform-specific meanings for 40 commonly misunderstood emoji obtained through Amazon Mechanical Turk ̶ A collection of REST API for easy access and integration 16
  17. 17. Nonuple Notation of Emoji • We propose a nine-tuple definition for emoji (ei) based on the emoji information available on the Web. ̶ ui – Unicode code point ̶ ni – Emoji name ̶ ci – Emoji short code ̶ di – Emoji definition ̶ Ki – Set of keywords ̶ Ii – Set of images ̶ Ri – Set of related emoji ̶ Hi – Set of categories ̶ Si – Set of senses with definitions 17 Raising Hands Emoji
  18. 18. Building EmojiNet 18 Building EmojiNet using Multiple Web Resources
  19. 19. Integrating Emoji Dictionary • A nearest neighborhood-based image processing algorithm (Santos 2010) was used to integrate Emoji Dictionary to EmojiNet • Two images sets were used: ̶ 18,615 images downloaded from Unicode.org representing 2,389 emoji ̶ 1,074 images downloaded from The Emoji Dictionary representing 1,074 emoji ̶ We use color intensities of each image to compute similarities between the images 19
  20. 20. Integrating Emoji Dictionary Cont. • Image processing algorithm in simple steps: ̶ Re-size each image to 300 X 300 pixels and divide each image to 25 non-overlapping regions of size 25 X 25 pixels ̶ Find average color intensity of each region by averaging R, G and B pixel color values ̶ Compare the color intensities of corresponding image regions and calculate the dissimilarity between the images using L2 distance ̶ Select the least dissimilar image as the match 20Source Emoji from Emoji Dictionary Emoji Suggestions from Unicode.org Based on the Similarity
  21. 21. Sense Extraction from Web Resources 21Extracting Emoji Sense Labels
  22. 22. Linking Sense Labels with BabelNet 22 Laugh(N): bn:00050198n (5) Laugh(N): bn:00050199n (3) Is Laugh(N) in EmojiNet? Laugh(N) = bn:00050198n Gun(N): bn:00042221n (6) Gun(N): bn:02379114n (1) Gun(N) = bn:00042221n
  23. 23. Evaluation • Resource linking based on image similarity performed with 96.27% accuracy ̶ 40 incorrect matches were identified out of 1,074 ̶ 13 family emoji, 9 face/person emoji and 8 clock emoji were identified incorrectly among others ̶ Error analysis revealed that the algorithm fails when the two compared images represent two different objects but similar in color. • Eg. – Clocks with arms displaying different times, Flags with slight changes 23
  24. 24. Evaluation Cont. 24 Most Frequent Sense Baseline Results # of total sense labels 12,904 # of disambiguated sense labels using MFS method 7,815 # of correctly disambiguated sense labels 6,673 # of incorrectly disambiguated sense labels 1,142 Accuracy of MFS-baseline method 85.38% Most Popular Sense Baseline Results # of total sense labels 12,904 # of disambiguated sense labels using MPS method 5,089 # of correctly disambiguated sense labels 4,106 # of incorrectly disambiguated sense labels 983 Accuracy of MFS-baseline method 80.68%
  25. 25. Evaluation Cont. 25 Aggregated Word Sense Disambiguation Evaluation Statistics Correct Incorrect Total Nouns 6,633 (86.64%) 1,022 (13.36%) 7,655 Verbs 2,331 (77.14%) 661 (22.86%) 2,892 Adjectives 1,915 (81.24%) 442 (18.76%) 2,357 Total 10,779 (83.53%) 2,125 (16.47%) 12,904
  26. 26. Summary • We created a machine-readable emoji sense repository • We enriched the sense definitions of each emoji sense with context words learned from Twitter and Google News* • We identified platform-specific emoji meanings for a set of 40 emoji that are commonly misinterpreted* • All our datasets are publically available at http://emojinet.knoesis.org/ with REST APIs * These can be downloaded separately from http://emojinet.knoesis.org/ 26
  27. 27. 2. Measuring Emoji Similarity Francesco Barbieri, Francesco Ronzano, Horacio Saggion. "What does this Emoji Mean? A Vector Space Skip-Gram Model for Twitter Emojis." In LREC. 2016. [PDF] Ben Eisner, Tim Rocktäschel, Isabelle Augenstein, Matko Bošnjak, Sebastian Riedel. "emoji2vec: Learning Emoji Representations from their Description." In Conference on Empirical Methods in Natural Language Processing, p. 48. 2016. [PDF] Henning Pohl, Christian Domin, Michael Rohs. "Beyond Just Text: Semantic Emoji Similarity Modeling to Support Expressive Communication👫📲😃." ACM Transactions on Computer-Human Interaction (TOCHI) 24, no. 1 (2017): 6. [PDF] Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran. A Semantics-Based Measure of Emoji Similarity. In 2017 IEEE/WIC/ACM International Conference on Web Intelligence (Web Intelligence 2017). Leipzig, Germany; 2017. [Kno.e.sis Library Page] | [PDF] | [BibTeX] 27
  28. 28. Emoji Similarity Problem 28 “Measuring the semantic similarity of emoji such that the measure reflects the likeness of their meaning, interpretation or intended use.” [Wijeratne et al., 2017]
  29. 29. Why Emoji Similarity • Measuring the Emoji Similarity can lead to improvements in numerous tasks including: ̶ 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 29
  30. 30. Challenges in Emoji Similarity Calculation • 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) 30 Different Emoji Pictures used for Rendering the Same Emoji (Miller et al., 2016)
  31. 31. Challenges in Emoji Similarity Calculation Cont. 31 Can we devise a notion of emoji similarity that reflects the meaning or intended use of the emoji?
  32. 32. Related Research • EmoTwi50 by Barbieri et al. ̶ Used Word2Vec to learn distributional semantics of emoji usage to calculate emoji similarity • emoji2vec by 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) • EmoSim508 by Wijeratne et al. ̶ Used emoji embeddings that encapsulates emoji meanings as described in EmojiNet to learn emoji semantics 32
  33. 33. EmoTwi50 by Barbieri et al. • Used 10 Million tweets to learn distributional semantics of emoji ̶ Created three distributions out of the 10 Million tweets • Calculated emoji similarity as the cosine similarity of word vectors learned over different parameter settings 33
  34. 34. EmoTwi50 by Barbieri et al. Cont. • Created a gold standard dataset of 50 emoji pairs ̶ 25 hand-selected highly correlated emoji pairs ̶ 25 random emoji pairs • 8 annotators evaluated the 50 emoji pairs for similarity and relatedness ̶ I consider the two emojis equivalent (similarity) – functional similarity ̶ I can imagine a situation in which I would use the two emojis together (relatedness) – topical similarity 34
  35. 35. EmoTwi50 Evaluation • Word vectors learn the best representations when the context vectors are small (3 to 6 words) and when the tweets are cleaned (stop words, URLs, Hashtags etc. are removed) 35
  36. 36. emoji2vec by Eisner et al. • Represented emoji meanings using CLDR emoji short names listed in Unicode Consortium Website ̶ On average, 4 words were to represent emoji meaning ̶ Word embeddings were used to learn distributional semantics of emoji 36
  37. 37. emoji2vec Learning Emoji Embeddings 37Learning emoji2vec Emoji Embedding Models using Word Vectors
  38. 38. emoji2vec Evaluation • Eisner et al. tested their emoji embedding models using a sentiment analysis baseline ̶ The 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 ̶ emoji2vec emoji embeddings outperformed embeddings learned by Barbieri et al. model 38
  39. 39. emoji2vec Evaluation Cont. 39 Accuracy of the Sentiment Analysis task using emoji2vec Emoji Embeddings Word Embedding Model Classification accuracy on sections of testing dataset All Tweets Tweets with Emoji Tweets /w top 10% Emoji Tweets /w bottom 10% Emoji N = 12,920 N = 2,295 N = 2,186 N = 308 RF SVM RF SVM RF SVM RF SVM Google News 57.5 58.5 46.0 47.1 47.3 45.1 44.7 43.2 Google News + Barbieri et al. 58.2 60.0 54.4 59.2 55.0 59.5 54.5 55.2 Google News + emoji2vec 59.5 60.5 54.4 59.2 55.0 59.5 54.5 55.2
  40. 40. emoji2vec Evaluation • Emoji2vec embeddings were used in an emoji analogy task (similar to the analogy task described by Mikolov et al.) 40
  41. 41. Phol et al. • Learned distributional semantics of emoji over a corpus of 21 Million tweets ̶ Used Word2vec tool to learn emoji embeddings • Use CLDR emoji annotations available in Unicode Consortium Website to calculate emoji similarity 41 Total Annotations – 3 Total Annotations – 4 Overlapping Annotations – 1 Emoji Similarity – 1 / (3 + 4) = 1/7
  42. 42. Phol et al. Evaluation • Created a dataset of 90 emoji pairs ̶ 45 random emoji pairs out of the top 5% similar emojis as per emoji embeddings ̶ 45 random emoji pairs out of the remaining emoji 42
  43. 43. EmoSim508 by Wijeratne et al. • Represented emoji meaning using emoji definitions extracted from 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 43
  44. 44. Representing Emoji Meaning using Different Emoji Definitions • Extract emoji definitions from EmojiNet 44 Extracting Emoji Definitions from EmojiNet
  45. 45. Emosim508 – Learning Emoji Embeddings 45Learning Emoji Embedding Models using Word Vectors
  46. 46. Ground Truth Data Creation • 110M Tweets were used to identify 508 most frequently co-occurred emoji pairs, which covered 25% of the dataset. Full emoji list – https://goo.gl/fvP7K9 46 Emoji Co-Occurrence Frequency Graph
  47. 47. Ground Truth Data Creation Cont. • 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 47
  48. 48. Intrinsic Evaluation • 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 48
  49. 49. Intrinsic Evaluation Cont. • 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 49 Spearman’s Rank Correlation Results
  50. 50. Extrinsic Evaluation • 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 ̶ Using EmojiNet knowledgebase resulted in ~9% improvement in accuracy of the sentiment analysis task 50
  51. 51. Extrinsic Evaluation Cont. 51 Accuracy of the Sentiment Analysis task using Emoji Embeddings Word Embedding Model Classification accuracy on sections of testing dataset All Tweets Tweets with Emoji Tweets /w top 10% Emoji Tweets /w bottom 10% Emoji N = 12,920 N = 2,295 N = 2,186 N = 308 RF SVM RF SVM RF SVM RF SVM Google News 57.5 58.5 46.0 47.1 47.3 45.1 44.7 43.2 Google News + emoji2vec 59.5 60.5 54.4 59.2 55.0 59.5 54.5 55.2 Google News + (Sense_Label) 60.3 63.3 55.0 61.8 56.8 62.3 54.2 59.0 Twitter + (Sense_Label) 60.7 63.6 57.3 60.8 57.5 61.5 56.1 58.4
  52. 52. Key Takeaways • Combining emoji sense knowledge with distributional semantics could improve the emoji embedding models ̶ Improved sentiment analysis by ~9% compared to models developed using only distributional semantics • 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 • Datasets is available at http://emojinet.knoesis.org/ 52
  53. 53. 3. Emoji Meaning Disambiguation 53 Donato, Giulia, and Patrizia Paggio. "Investigating Redundancy in Emoji Use: Study on a Twitter Based Corpus." In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 118-126. 2017. [PDF] Noa Na'aman, Hannah Provenza, Orion Montoya. "Varying Linguistic Purposes of Emoji in (Twitter) Context." In Proceedings of ACL 2017, Student Research Workshop, pp. 136-141. 2017. [PDF] Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran. EmojiNet: An Open Service and API for Emoji Sense Discovery. In 11th International AAAI Conference on Web and Social Media (ICWSM 2017). Montreal, Canada; 2017. [Kno.e.sis Library Page] | [PDF] | [BibTeX]
  54. 54. Emoji Sense Disambiguation Problem “The ability to identify the meaning of an emoji in the context of a message in a computational manner” [Wijeratne et al., 2017]. It’s a difficult problem [Miller et al., 2017]. 54 Image Source – https://goo.gl/rjS1hX I Look
  55. 55. Why Emoji Sense Disambiguation? • Improve the accuracy of sentiment analysis and other emoji understanding tasks 55 Happy or Sad?
  56. 56. Challenges in Emoji Sense Disambiguation • Emoji usage is complicated as emoji were not defined with clear pragmatics ̶ Emoji are used to replace words ̶ Emoji are used to emphasize the words in a message • Difficult to create training datasets to train supervised models ̶ Annotator agreement is generally low compared to text annotation tasks ̶ Large annotated corpora is not yet available for sense disambiguation tasks 56
  57. 57. Donato et al. – Redundancy of Emoji Usage • Studied how emoji are used along with text messages using a Twitter corpus • Identified three main usage classes ̶ Redundant, Non-redundant, and Non-redundant + POS ̶ Created an annotated corpus but didn’t develop classification models 57
  58. 58. MojiSem by Na’aman et al. • Studied how emoji are used along with text messages using a Twitter corpus • Identified three main usage classes ̶ Function, Content, and Multimodal ̶ Created an annotated corpus ̶ Developed classification models to automatically distinguish between the three classes 58
  59. 59. MojiSem Emoji Usage Classes 59 • Function – These are function words in an utterance. E.g., – prepositions, auxiliaries, punctuations etc. • • Content – Lexical words or phrases that are part of the main sentence. These have natural PoS such as: noun, verb, adj, adv, other • Multimodal – Characters that enrich a grammatically-complete text with markers of affect or stance. . E.g., – attitude, topic, gesture
  60. 60. MojiSem Dataset 60 • Four annotators annotated 557 tweets with 775 emoji occurrences • Annotated text were then used to train supervised classifications models to distinguish between the three emoji usage classes
  61. 61. MojiSem Classifiers 61
  62. 62. Wijeratne et al. Emoji Sense Disambiguation • Currently, no labeled datasets available to solve the emoji sense disambiguation in a supervised setting 62 Sense Context words extracted from EmojiNet for each Sense Pray (verb) worship, thanksgiving, saint, pray, higher, god, confession High five (noun) Palm, high, hand, slide, celebrate, raise, person, head, five T1 – Pray for my family God gained an angel today T2 – Hard to win, but we did it man Lets celebrate! Context Words Extracted from EmojiNet
  63. 63. Wijeratne et al. Emoji Sense Disambiguation Cont. • We selected 25 most commonly misunderstood emoji and selected 50 tweets for each emoji ̶ Used Simplified LESK algorithm for disambiguation ̶ Context words were learned for each emoji sense definition using Twitter and Google News-based word embedding models ̶ Twitter-based embeddings outperform others 63 Top 10 Emoji based on the Emoji Sense Disambiguation Accuracy (in % values)
  64. 64. Key Takeaways • Emoji usage in text is complicated ̶ Emoji exhibits some language-like behavior but mostly they are used to express emotions • Creating emoji sense disambiguation corpora can be expensive ̶ Need to annotate text for each sense of each emoji • Emoji sense repositories can be used to cut down the efforts on data annotation 64
  65. 65. What was not Covered • There are many other challenging emoji understanding tasks ̶ Emoji prediction ̶ Applications of emoji analysis • We are working on using EmojiNet data for emoji prediction 65
  66. 66. References 1. Miller, Hannah, Jacob Thebault-Spieker, Shuo Chang, Isaac Johnson, Loren Terveen, and Brent Hecht. "Blissfully happy” or “ready to fight”: Varying Interpretations of Emoji." Proceedings of ICWSM 2016 (2016). [PDF] 2. Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran. EmojiNet: An Open Service and API for Emoji Sense Discovery. In 11th International AAAI Conference on Web and Social Media (ICWSM 2017). Montreal, Canada; 2017. [Kno.e.sis Library Page] | [PDF] | [BibTeX] 3. Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran. EmojiNet: Building a Machine Readable Sense Inventory for Emoji. In 8th International Conference on Social Informatics (SocInfo 2016). Bellevue, WA, USA; 2016. [Kno.e.sis Library Page] | [PDF] | [BibTeX] 4. Umashanthi Pavalanathan, Jacob Eisenstein. "Emoticons vs. emojis on Twitter: A causal inference approach." arXiv preprint arXiv:1510.08480 (2015). [PDF] 5. Francesco Barbieri, Francesco Ronzano, Horacio Saggion. "What does this Emoji Mean? A Vector Space Skip-Gram Model for Twitter Emojis." In LREC. 2016. [PDF] 6. Ben Eisner, Tim Rocktäschel, Isabelle Augenstein, Matko Bošnjak, Sebastian Riedel. "emoji2vec: Learning Emoji Representations from their Description." In Conference on Empirical Methods in Natural Language Processing, p. 48. 2016. [PDF] 7. Henning Pohl, Christian Domin, Michael Rohs. "Beyond Just Text: Semantic Emoji Similarity Modeling to Support Expressive Communication👫📲😃." ACM Transactions on Computer-Human Interaction (TOCHI) 24, no. 1 (2017): 6. [PDF] 8. Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran. A Semantics-Based Measure of Emoji Similarity. In 2017 IEEE/WIC/ACM International Conference on Web Intelligence (Web Intelligence 2017). Leipzig, Germany; 2017. [Kno.e.sis Library Page] | [PDF] | [BibTeX] 9. Tomas Mikolov, Wen-tau Yih, Geoffrey Zweig. "Linguistic regularities in continuous space word representations." In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746-751. 2013. [PDF] 66
  67. 67. 8. Lakshika Balasuriya, Sanjaya Wijeratne, Derek Doran, Amit Sheth. Finding Street Gang Members on Twitter, In The 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016). San Francisco, CA, USA; 2016. [Kno.e.sis Library Page] | [PDF] | [BibTeX] 9. Donato, Giulia, and Patrizia Paggio. "Investigating Redundancy in Emoji Use: Study on a Twitter Based Corpus." In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 118-126. 2017. [PDF] 10. Noa Na'aman, Hannah Provenza, Orion Montoya. "Varying Linguistic Purposes of Emoji in (Twitter) Context." In Proceedings of ACL 2017, Student Research Workshop, pp. 136-141. 2017. [PDF] 11. Miller, Hannah, Daniel Kluver, Jacob Thebault-Spieker, Loren Terveen, Brent Hecht. "Understanding emoji ambiguity in context: The role of text in emoji-related miscommunication." In 11th International Conference on Web and Social Media, ICWSM 2017. AAAI Press, 2017. [PDF] 12. Petra Kralj Novak, Jasmina Smailović, Borut Sluban, Igor Mozetič. "Sentiment of emojis." PloS one 10, no. 12 (2015): e0144296. [PDF] 67 References Cont.
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