class: center, middle, inverse, title-slide # Skin Tone Emoji and Sentiment on Twitter ### <div class="line-block">Steven Coats<br /> English Philology, University of Oulu<br /> <a href="mailto:steven.coats@oulu.fi">steven.coats@oulu.fi</a></div> ### <div class="line-block"><br /> 3rd DHN Conference, Helsinki<br /> March 7th, 2018</div> --- class: inverse, center, middle background-image: url(https://cc.oulu.fi/~scoats/oululogoRedTransparent.png); background-repeat: no-repeat; background-size: 80px 57px; background-position:right top; exclude: true --- layout: true <div class="my-header"><img border="0" alt="W3Schools" src="https://cc.oulu.fi/~scoats/oululogonewEng.png" width="80" height="80"></div> <div class="my-footer"><span>Steven Coats &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp; &emsp;&emsp;&emsp;Skin Tone Emoji and Sentiment on Twitter | DHN 18</span></div> --- <div class="my-header"><img border="0" alt="W3Schools" src="https://cc.oulu.fi/~scoats/oululogonewEng.png" width="80" height="80"></div> <div class="my-footer"><span>Steven Coats &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp; &emsp;&emsp;&emsp;Skin Tone Emoji and Sentiment on Twitter | DHN 18</span></div> ## Outline 1. Emoticons and Emoji 2. Skin tone emoji 3. Global distribution of skin tone emoji 4. Skin tone emoji and sentiment 5. Skin tone emoji and word embeddings --- ## Emoticons and Emoji -- - Emoticons: Pictorial representations of (mainly) facial expressions, created with sequences of (mainly) ASCII or Latin-1 characters &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;:)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;:D&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;:-/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;:^)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(^\_^)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ಠ\_ಠ&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ʕ •ᴥ•ʔ&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;( ͡° ͜ʖ ͡°) -- - Emoji: Pictorial representations in graphical form. Origins in Japan in 1990s, introduced into Unicode late 2000s as dedicated code points. Currently 2,789 unique emoji, more with every Unicode update. .center[ <br>].small[Glyphs from [twemoji](https://github.com/twitter/twemoji)] -- - Emoji are used in computer-mediated communication in most languages, making them interesting for NLP --- <div class="my-header"><img border="0" alt="W3Schools" src="https://cc.oulu.fi/~scoats/NewLogoRussianPNG1.png" width="80" height="80"></div> <div class="my-footer"><span>Steven Coats &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp; &emsp;&emsp;&emsp;Skin Tone Emoji and Sentiment on Twitter | DHN 18</span></div> ## Skin tone emoji Since Unicode 8.0 (June 17, 2015), skin tone characters are part of Unicode -- .pull-left30[ .small[[source](https://www.arpansa.gov.au/sites/g/files/net3086/f/legacy/pubs/RadiationProtection/FitzpatrickSkinType.pdf)]] -- .pull-right70[ Emoji Modifier Fitzpatrick Type-1-2<br> Emoji Modifier Fitzpatrick Type-3<br> Emoji Modifier Fitzpatrick Type-4<br> Emoji Modifier Fitzpatrick Type-5<br> Emoji Modifier Fitzpatrick Type-6<br> ] --- ## Skin tone emoji use Skin tone is shown using sequences of Unicode characters .pull-left[  +  = <br>  +  = <br>  +  = <br>  +  = <br>  +  =  ] .pull-right[ <br><br> \U0001f478\U0001f3fb <br><br><br> \U0001f478\U0001f3fc <br><br><br> \U0001f478\U0001f3fd <br><br><br> \U0001f478\U0001f3fe <br><br><br> \U0001f478\U0001f3ff <br><br> ] --- ## Emoji sequences Since Unicode 9.0 (late 2016), emoji sequences can also be used to indicate activities, professions, groups, etc. These can usually be combined with skin tone as well. --  +  =  --  +  +  =  -- Sequences can utilize additional **zero-width joiner** and **variation selector** code points to show that the sequence is to be parsed as one character  = \U0001f9d6\U0001f3fb\U0000200d\U00002642\U0000fe0f -- - Parsing and tokenization of emoji sequences can present difficulties --- ## Research questions How are these skin tone emoji being used globally? - How often do users select a skin tone variant compared to a default version? - Which skin tones are being used? -- Emoji and sentiment - What does emoji use tell us about sentiment? (Kralj-Novak et al. 2015) - Is there a relationship between skin tone emoji and sentiment? -- Meanings associated with individual emoji types - Do skin tone emoji types exhibit similar semantic profiles? - Word embeddings to explore (skin tone) emoji meanings --- ## Data collection and methods - 653,457,659 tweets with *place* attributes collected from Twitter's Streaming API from November 2016 – June 2017 (retweets excluded) -- - Dictionary of **potential** skin tone emoji used to count occurrences of the types that can be modified with skin tone and their values - Median and average skin tone values per country calculated (1 = Emoji Modifier Fitzpatrick Type-1-2, 5 = Emoji Modifier Fitzpatrick Type-6) -- - Overview of skin tone use geographically, correlation of skin tone and sentiment (using Kralj-Novak et al. sentiment dictionary), word embeddings to investigate skin tone emoji meanings (tokenization issue) --- ## Global skin tone emoji summary statistics .small[ <div id="htmlwidget-ac13648f7a58a4ae66c1" style="width:100%;height:auto;" class="datatables html-widget"></div> <script type="application/json" data-for="htmlwidget-ac13648f7a58a4ae66c1">{"x":{"filter":"none","data":[["Andorra","United Arab Emirates","Afghanistan","Antigua and Barbuda","Anguilla","Albania","Armenia","Angola","Antarctica","Argentina","American Samoa","Austria","Australia","Aruba","Åland Islands","Azerbaijan","Bosnia and Herzegovina","Barbados","Bangladesh","Belgium","Burkina Faso","Bulgaria","Bahrain","Burundi","Benin","Saint Barthélemy","Bermuda","Brunei Darussalam","Bolivia (Plurinational State of)","Bonaire, Sint Eustatius and Saba","Brazil","Bahamas","Bhutan","Bouvet Island","Botswana","Belarus","Belize","Canada","Cocos (Keeling) Islands","Congo (Democratic Republic of the)","Central African Republic","Republic of the Congo","Switzerland","Côte d'Ivoire","Cook Islands","Chile","Cameroon","China","Colombia","Costa Rica","Cuba","Cabo Verde","Curaçao","Christmas 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da Cunha","Slovenia","Slovakia","Sierra Leone","San Marino","Senegal","Somalia","Suriname","Sao Tome and Principe","El Salvador","Sint Maarten (Dutch part)","Syrian Arab Republic","Swaziland","Turks and Caicos Islands","Chad","French Southern Territories","Togo","Thailand","Tajikistan","Tokelau","Timor-Leste","Turkmenistan","Tunisia","Tonga","Turkey","Trinidad and Tobago","Tuvalu","Taiwan, Province of China[a]","Tanzania, United Republic of","Ukraine","Uganda","United States Minor Outlying Islands","United States of America","Uruguay","Uzbekistan","Holy See","Saint Vincent and the Grenadines","Venezuela (Bolivarian Republic of)","Virgin Islands (British)","Virgin Islands (U.S.)","Viet Nam","Vanuatu","Wallis and Futuna","Samoa","Kosovo","Yemen","Mayotte","South Africa","Zambia","Zimbabwe"],[26072,2768770,25959,87015,3238,31198,44311,105193,19398,20023675,15274,509846,4459273,25499,1443,136739,125271,146395,245599,1591107,9932,155157,736147,4140,17039,3523,20827,85827,132929,2911,92987119,159771,5465,4656,219901,1066410,16102,9000826,501,58250,3987,26621,741929,129692,1347,4558678,160593,808971,5757491,942521,72553,7180,14929,1526,242284,397113,4407730,5999,455117,6729,895409,542599,1389597,74163,3682071,393,1691,15822869,12397,788234,4388,2399,1759,3684,13825568,18426,35837868,14610,94353,18436,1952,788924,22513,5183,8239,12570,86551,2864,878000,422,514045,109863,721,9702,489147,6,187786,185198,93233,253188,12767530,2247618,637865,32853,8003768,201,389394,279761,142086,6515934,1969,364207,444110,38598876,928242,51159,87266,384,1094,9462,20045,2073390,3354375,24246,321268,29888,471534,25719,1833,210269,8789,22552,56948,62499,247875,338483,375120,33668,38933,87086,7918,21129,1781,91410,14820,63028,165641,48125,23850,90528,8895,820,69644,44980,92793,42635,11807645,14787098,79236,146724,26991,9135,463,4139055,356352,3622008,539832,344630,372,457,812851,567037,651686,1657105,11553,8853,20808246,1489179,1896064,731,106,42615,4385496,973,1155869,881302,26053,268815,529377,9699523,40682,9181752,1591,5672,227640,1676469,1414131,386,148543,89743,7761,4368,119160,21400,10312,983,242918,9089,61622,22024,24669,4521,459,12635,11384462,4785,18,3282,2126,120493,2701,19218332,200210,130,555566,241163,1685609,215945,627,201361543,2956948,50244,15274,15063,4608092,27930,10901,351796,1759,934,4623,73378,85060,4589,5505798,106869,107156],[2771,192935,871,2628,247,1185,2534,12125,53,1836721,1722,27338,242131,1450,30,7045,3743,6093,13672,86770,566,5766,64532,146,1199,532,1486,5426,9750,183,3763962,7615,452,259,23896,52809,723,381566,0,3309,53,2174,38693,9338,65,358399,17198,30830,416142,95018,3391,510,819,45,17541,19751,245991,204,30600,460,68607,76184,111417,4903,509467,9,19,1086395,400,32823,362,114,32,83,763565,1230,2497332,691,6495,1368,104,75407,1279,121,540,943,5554,269,37862,1,36674,7525,19,586,21716,0,16002,8831,11454,15315,1101959,133706,16313,2365,617396,27,23641,9783,4445,325956,141,15082,37227,2228136,53963,2258,3715,1,21,646,256,56056,302685,1420,13605,1941,24726,965,28,18783,574,3337,1628,3248,12671,24151,31287,2403,1104,1506,616,796,123,2791,1336,2934,6913,3142,1293,5164,278,32,4284,2410,8997,3455,965782,811196,8547,12190,840,194,0,242083,27313,130830,33637,22127,3,22,35903,52673,52690,113980,508,1318,1269572,58287,75377,55,3,1638,147838,45,130786,57093,1789,19556,14949,445123,2824,612554,27,310,28648,76325,69844,4,6063,7220,438,170,8663,626,669,46,15858,444,8820,1906,1962,100,0,833,399332,103,0,134,96,8997,201,1005601,15723,1,31601,20209,73437,17045,0,11153159,241163,2000,709,616,153560,1565,698,14038,66,4,406,3137,3019,137,636327,11571,7523],[909,121591,482,1988,94,477,384,8462,16,290912,1317,9364,108017,939,4,2176,1252,4234,1335,40868,269,1953,43489,61,561,224,1270,3667,2530,139,1497697,5961,59,2,6971,18398,528,185116,0,1523,42,1082,17207,5454,52,141571,9471,15349,134810,54867,1655,327,485,31,7822,8481,76957,141,12090,247,29897,4257,37896,1317,149432,4,14,361905,108,9195,141,92,13,17,371576,557,1387807,384,2204,876,73,38058,664,46,397,455,3685,32,8919,1,12959,6006,0,274,9495,0,6965,1668,6644,6688,225206,62975,6081,1098,82856,7,8325,3592,2364,129988,81,9070,17985,445699,11298,1177,1932,0,10,548,32,21300,220995,1040,4674,997,8917,482,17,4356,275,582,655,1372,6009,13180,7751,988,407,482,197,182,103,438,480,1319,2098,1739,722,3308,58,18,1634,861,4446,1814,499003,432322,3525,6436,552,58,0,92415,12124,54939,10215,2171,0,21,18319,37503,23556,38986,287,123,581922,10096,17891,2,1,1355,64494,21,37959,40067,664,3329,2970,255321,1131,389881,6,203,13646,34468,40646,0,2600,2241,296,96,5943,158,244,30,3134,336,3839,504,1439,46,0,243,210751,72,0,42,36,1886,96,356020,10116,1,8488,10579,29535,4426,0,8605451,78953,613,451,294,41530,1420,659,3807,19,1,118,1708,779,49,251576,4467,3941],[0.33,0.63,0.55,0.76,0.38,0.4,0.15,0.7,0.3,0.16,0.76,0.34,0.45,0.65,0.13,0.31,0.33,0.69,0.1,0.47,0.48,0.34,0.67,0.42,0.47,0.42,0.85,0.68,0.26,0.76,0.4,0.78,0.13,0.01,0.29,0.35,0.73,0.49,0,0.46,0.79,0.5,0.44,0.58,0.8,0.4,0.55,0.5,0.32,0.58,0.49,0.64,0.59,0.69,0.45,0.43,0.31,0.69,0.4,0.54,0.44,0.06,0.34,0.27,0.29,0.44,0.74,0.33,0.27,0.28,0.39,0.81,0.41,0.2,0.49,0.45,0.56,0.56,0.34,0.64,0.7,0.5,0.52,0.38,0.74,0.48,0.66,0.12,0.24,1,0.35,0.8,0,0.47,0.44,0,0.44,0.19,0.58,0.44,0.2,0.47,0.37,0.46,0.13,0.26,0.35,0.37,0.53,0.4,0.57,0.6,0.48,0.2,0.21,0.52,0.52,0,0.48,0.85,0.12,0.38,0.73,0.73,0.34,0.51,0.36,0.5,0.61,0.23,0.48,0.17,0.4,0.42,0.47,0.55,0.25,0.41,0.37,0.32,0.32,0.23,0.84,0.16,0.36,0.45,0.3,0.55,0.56,0.64,0.21,0.56,0.38,0.36,0.49,0.53,0.52,0.53,0.41,0.53,0.66,0.3,0,0.38,0.44,0.42,0.3,0.1,0,0.95,0.51,0.71,0.45,0.34,0.56,0.09,0.46,0.17,0.24,0.04,0.33,0.83,0.44,0.47,0.29,0.7,0.37,0.17,0.2,0.57,0.4,0.64,0.22,0.65,0.48,0.45,0.58,0,0.43,0.31,0.68,0.56,0.69,0.25,0.36,0.65,0.2,0.76,0.44,0.26,0.73,0.46,0,0.29,0.53,0.7,0,0.31,0.38,0.21,0.48,0.35,0.64,1,0.27,0.52,0.4,0.26,0,0.77,0.33,0.31,0.64,0.48,0.27,0.91,0.94,0.27,0.29,0.25,0.29,0.54,0.26,0.36,0.4,0.39,0.52],[1.48,1.75,3.01,3.36,2.88,1.91,1.58,3.47,2.44,1.87,2.15,1.75,1.88,2.28,2,1.43,1.97,3.47,2.07,2.03,3.53,1.78,1.75,3.43,3.75,1.99,3.01,1.54,1.81,1.63,1.98,3.24,1.34,4,3.05,1.91,2.09,2.12,0,3.01,1.83,3.77,1.97,3.67,2.46,1.65,3.62,1.94,1.89,1.96,2.69,2.5,2.19,1.97,2.03,1.79,1.8,3.19,1.79,3.21,1.99,2.13,1.71,1.91,1.46,4,3.57,1.89,3.27,1.77,2.28,1.88,2.62,2.06,2.22,3.28,1.89,3.46,1.7,3.42,1.3,3.54,1.98,1.8,3.47,3.71,3.44,3.53,1.74,4,1.79,2.07,0,2.96,1.68,0,1.67,1.81,3.31,1.93,1.43,1.74,2.01,1.68,1.53,3.86,1.57,1.52,1.87,1.57,1.95,3.41,1.46,1.86,3.51,1.64,1.59,0,2.5,3.33,1.56,1.63,1.76,2.82,1.65,1.31,1.53,2.9,1.71,1.69,3.56,3.39,2.07,2.42,1.95,1.53,1.96,1.79,1.47,1.74,2.35,2.4,2.51,1.72,3.05,1.69,1.71,1.36,1.9,3.32,2.29,4,2.14,2.63,2.12,3.74,1.74,1.46,3.64,3.41,1.36,2.72,0,3.26,1.65,1.83,1.91,1.82,0,3.62,2.17,1.58,1.8,1.79,2.32,2.7,1.47,1.72,1.72,3,2,1.57,2.18,1.86,1.73,1.72,2.72,1.83,1.59,1.83,3.64,1.58,3.17,2.24,2.65,1.79,1.74,0,2.09,1.61,3.65,1.71,3.8,3.34,3.39,3.2,1.81,2.94,1.32,2.96,3.28,3.72,0,3.8,1.35,1.72,0,1.57,1.64,1.63,3.17,1.58,3.36,1,1.5,3.53,1.89,3.64,0,2.71,1.92,1.46,1.71,3.59,2.03,3.72,3.03,1.65,2.53,4,2.64,1.73,1.62,3.18,3.34,3.3,3.56],[1,1,3,3,3,2,1,4,3,2,2,1,2,2,2,1,2,4,1,2,4,1,1,4,4,2,3,1,2,1,2,3,1,4,3,2,2,2,null,3,2,4,2,4,3,1,4,1,2,2,3,2,2,1,2,2,2,3,2,3,2,2,1,2,1,5,4,2,4,1,2,2,3,2,2,3,2,4,1,4,1,4,2,1,4,4,4,4,1,4,2,2,null,3,1,null,1,2,3,2,1,1,2,2,1,4,1,1,2,1,2,3,1,1,4,1,1,null,2.5,4,1,1,1,3,1,1,1,3,2,1,4,3,2,2,2,1,2,2,1,1,2,2,3,1,3,1,1,1,2,3,2,4,2,3,2,4,1,1,4,4,1,3,null,3,1,2,2,1,null,4,2,1,1,2,2,3,1,1,1,3,2,1,2,1,1,1,3,2,1,2,4,1,4,2,3,2,1,null,2,1,4,1,4,4,4,3,2,3,1,3,3,4,null,4,1,2,null,1,1,1,3,1,4,1,1,4,2,4,null,3,2,1,1,4,2,4,4,1,2,4,3,2,1,3,3,4,4]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th>Country<\/th>\n <th>nTweets<\/th>\n <th>Pot_skin_tone<\/th>\n <th>nSkintone<\/th>\n <th>propSkinton<\/th>\n <th>avg<\/th>\n <th>med<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"pageLength":247,"scrollY":"400px","caption":{"name":"caption","attribs":{"style":"caption-side: bottom; text-align: center;"},"children":["Table 1: ",{"name":"em","attribs":[],"children":["Counts of tweets, potential skin tone emoji, and skin tone emoji by country"]}]},"columnDefs":[{"className":"dt-right","targets":[1,2,3,4,5,6]}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[10,25,50,100,247]}},"evals":[],"jsHooks":[]}</script> ] --- ## Proportion of potential skin tone emoji assigned skin tone <div class="midcenter"> <iframe src="https://cc.oulu.fi/~scoats/chartProportion1.html" style="max-width = 100%" sandbox="allow-same-origin allow-scripts" width="900" height="500" scrolling="yes" seamless="seamless" frameborder="0" align="middle"> </iframe> </div> --- ## Median skin tone values <div class="midcenter"> <iframe src="https://cc.oulu.fi/~scoats/chartMedianST1.html" style="max-width = 100%" sandbox="allow-same-origin allow-scripts" width="100%" height="500" scrolling="yes" seamless="seamless" frameborder="0" align="middle"> </iframe> </div> --- <div class="my-header"><img border="0" alt="W3Schools" src="https://cc.oulu.fi/~scoats/NewLogoRussianPNG1.png" width="80" height="80"></div> <div class="my-footer"><span>Steven Coats &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp; &emsp;&emsp;&emsp;Skin Tone Emoji and Sentiment on Twitter | DHN 18</span></div> ## Global distribution of skin colors .small[[source](https://sruk.org.uk/skin-color-an-example-of-adaptation-to-the-environment/)] --- ## Emoji sentiment rankings (Kralj-Novak et al. 2015) - L1 Annotators categorized tweets containing emoji in 13 European languages as "negative", "neutral", or "positive" - Aggregate statistics were used to assign sentiment values to individual emoji - This dictionary was applied to evaluate sentiment in my data --- ## Emoji sentiment rankings (Kralj-Novak et al. 2015) <div class="midcenter"> <iframe src="https://kt.ijs.si/data/Emoji_sentiment_ranking/" style="max-width = 100%" sandbox="allow-same-origin allow-scripts" width="100%" height="550" scrolling="yes" seamless="seamless" frameborder="0" align="middle"> </iframe> </div> --- ## Calculation of mean sentiment by country/territory for tweets with potential skin tone emoji - ~25m tweets with potential skin tone emoji - Tweets stripped of usernames, URLs, and hashtags, then tokenized - Mean values per country/territory  --- ## Correlation of tweet sentiment with skin tone emoji .pull-left[  ] .pull-right[  ] --- ## Word embeddings - Distributional hypothesis (Harris 1968) - Collocational information can be represented with vectors of co-occurrence probabilities - Similarity of collocational context (and thus meaning) for any two types can be quantified using cosine similarity - For types `\(A\)` and `\(B\)`, corresponding to vectors `\(\mathbf{A}\)` and `\(\mathbf{B}\)`: `$$\text{similarity} = \cos(\theta) = {\mathbf{A} \cdot \mathbf{B} \over \|\mathbf{A}\| \|\mathbf{B}\|} = \frac{ \sum\limits_{i=1}^{n}{A_i B_i} }{ \sqrt{\sum\limits_{i=1}^{n}{A_i^2}} \sqrt{\sum\limits_{i=1}^{n}{B_i^2}} }$$` - Based on a span of 5 tokens to the left and right and at least 10 occurrences in the tweets with potential skin tone --- ## Cosine similarity to skin emoji codepoints, 1,000 most frequent types in corpus¹ .small[ <div id="htmlwidget-77f5e77466bdec161743" style="width:100%;height:auto;" class="datatables html-widget"></div> <script type="application/json" 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class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>type<\/th>\n <th>1<\/th>\n <th>2<\/th>\n <th>3<\/th>\n <th>4<\/th>\n <th>5<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"pageLength":1000,"scrollY":"350px","caption":{"name":"caption","attribs":{"style":"caption-side: bottom; text-align: center;"},"children":["Table 1: ",{"name":"em","attribs":[],"children":["Counts of tweets, potential skin tone emoji, and skin tone emoji by country"]}]},"columnDefs":[{"targets":[1,2,3,4,5],"render":"function(data,row,type,meta) { \n if (data.toString().match(/http/))\n {\n return \"<img src='\"+data+\"'/>\";\n }\n else {\n return data;\n }\n }"},{"className":"dt-right","targets":[2,3,4,5,6]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[10,25,50,100,1000]}},"evals":["options.columnDefs.0.render"],"jsHooks":[]}</script> .footnote[[1]~25m tweets with potential skin tone emojis.] ] --- ## Tokenizing emoji sequences - Python script incorporating elements from [nltk.tokenize.casual](https://www.nltk.org/_modules/nltk/tokenize/casual.html), [tinysegmenter](https://pypi.python.org/pypi/tinysegmenter) for Japanese, [jieba](https://pypi.python.org/pypi/jieba/) for Mandarin, and [emojione](https://github.com/emojione/emojione) data for sequences -- 'This is a tokenizer test ' -- - ['This',&nbsp;&nbsp;'is',&nbsp;&nbsp;'a',&nbsp;&nbsp;'tokenizer',&nbsp;&nbsp;'test',&nbsp;&nbsp;'',&nbsp;&nbsp;'',&nbsp;&nbsp;'',&nbsp;&nbsp;''] = bad! - ['This',&nbsp;&nbsp;'is',&nbsp;&nbsp;'a',&nbsp;&nbsp;'tokenizer',&nbsp;&nbsp;'test',&nbsp;&nbsp;'',&nbsp;&nbsp;''] = good! -- '私は絵文字が大好き！' -- - ['私は絵文字が大好き',&nbsp;&nbsp;'！',&nbsp;&nbsp;'',&nbsp;&nbsp;''] = bad! - ['私',&nbsp;&nbsp;'は',&nbsp;&nbsp;'絵文字',&nbsp;&nbsp;'が',&nbsp;&nbsp;'大好き',&nbsp;&nbsp;'！',&nbsp;&nbsp;''] = good! --- ## 400 to 2 dimensions: t-SNE (van der Maaten and Hinton 2008) of emoji vectors <div class="midcenter"> <iframe src="https://cc.oulu.fi/~scoats/tsneEmoji_composed_060318.html" style="max-width = 100%" sandbox="allow-same-origin allow-scripts" width="100%" height="800" scrolling="yes" seamless="seamless" frameborder="0" align="middle"> </iframe> </div> --- ## Summary - About half of emojis that can take skin tone have skin tone – more popular in Anglophone countries - Lighter skin tone emoji are favored in Asia, the Middle East, and parts of Latin America -- - Negative correlation between sentiment and darker skin tone (Helliwell et al. 2017, Ljubešić and Fišer 2016) - Light median skin tone values: prevailing cultural standards? (Peltzer et al. 2016; Swami et al. 2008, Li et al. 2008, Sahay & Piran 1997) - Darker skin tone emoji are closer in meaning to informal (AAVE) English lexical items -- - Kral-Novak et al. sentiment dictionary only up to Unicode 6.0 (2014), only European languages, low levels of inter-annotator agreement - More detailed examination possible (USA, Europe, Nordics, E. Asia, e.g.) - Analysis of evaluative use or correlation with affective language (swearing/profanity) --- #Thank you! --- ### References .small[ .hangingindent[ Davis, M., and Edberg, P. (2015). [Unicode emoji](https://unicode.org/reports/tr51/) (Unicode Technical Standard #51). Harris, Z. (1968). *Mathematical structures of language*. New York: Interscience. Helliwell, J. F., Huang, H., and Wang, S. (2017). The social foundations of world happiness. In: Helliwell, J. F., Layard, R., and Sachs, J. (eds.), *Word Happiness Report 2017*. New York: Columbia University Center for Sustainable Development. Kralj-Novak, P., Smailovic, J., Sluban, B., and Mozetic, I. (2015). [Sentiment of emojis](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0144296). *PLoS ONE* 10(12). Li, E., Min, H., Belk, R., Kimura, J., and Bahl, S. (2008). Skin lightening and beauty in four Asian cultures. In: Lee, A., and Soman, D. (eds.), *Advances in Consumer Research Volume 35*, pp. 444–449. Duluth, MN: Association for Consumer Research. Ljubešić, N., and Fišer, D. (2016). A global analysis of emoji usage. In: *Proceedings of the 10th Web as Corpus Workshop (WAC-X) and the EmpiriST Shared Task*, pp. 82–89. Stroudsburg, PA: Association for Computational Linguistics. van der Maaten, L., and Hinton, G. (2008). Visualizing High-Dimensional Data Using t-SNE. *Journal of Machine Learning Research* 9:2579–2605. Peltzer, K., Pengpid, D., and James, C. (2016). The globalization of whitening: prevalence of skin lighteners (or bleachers) use and its social correlates among university students in 26 countries. *International Journal of Dermatology* 55(2), 165–172. Sahay S., and Piran, N. (1997). Skin-color preferences and body satisfaction among South Asian-Canadian and European-Canadian female university students. *Journal of Social Psychology* 137(2), 161–171. Swami, V., Furnham, A., and Joshi, K. (2008). The influence of skin tone, hair length, and hair colour on ratings of women's physical attractiveness, health and fertility. *Scandinavian Journal of Psychology* 49, 429–437. ]]