The first dataset I found was the Emoji Sentiment Ranking<\/a> data available on Kaggle. This dataset was created in 2015 and is comprised of 751 emoji characters and their assigned sentiment. Sentiment for these emojis was calculated based on 70,000 tweets that were labeled by human annotators in 13 European languages. Sentiment is broken down into 3 classes: positive, negative and neutral sentiment. Emojis are then assigned values based on the number of times annotators found that they expressed positive, negative and neutral sentiment and instructions on how to calculate an overall sentiment score was provided by the authors. This dataset included records for:<\/p>\n\n\n\n The second dataset, Tweets With Emojis – #MeToo<\/a>, was downloaded from data.world and included a sample of tweets from the #MeToo hashtag. Tweets were pulled from a sample of 28,629 English-language tweets on Oct. 16, 2017. This dataset included records for:<\/p>\n\n\n\n I wanted to join the two datasets together so that the emojis in the Tweets With Emojis – #MeToo<\/a> tweets would correspond to the sentiment scores from the Emoji Sentiment Ranking<\/a> data. To do this, I needed to check that the emoji UNICODE names for both datasets matched so that I could use this as a primary key to join the two tables. I worked with OpenRefine to perform the data cleansing and I used find and replace for any UNICODE names that didn\u2019t match the UNICODE names from the Emoji Sentiment Ranking data. Since the sentiment ranking dataset was from 2015 and the #MeToo dataset was from 2017, some of the emojis used in 2017 weren\u2019t around in 2015 therefore those emojis without a sentiment analysis were removed as records. After joining the two tables together, I deleted any records that didn\u2019t contain emojis in the tweet. Some tweets had more than one emoji, so I used the split multi-valued cells function in the \u2018Emoji\u2019 column to create a new record for each emoji.<\/p>\n\n\n\n After data cleansing, the total number of tweets included in the analysis was 1,218, with 1,633 emojis. The final step in the process was done in Excel to calculate the sentiment score based on the calculation outlined by Kralj et al. (2020).<\/p>\n\n\n\n I knew that I wanted to visualize a part-to-whole relationship to show the breakdown of each emoji\u2019s positive, negative and neutral sentiment, so I drew inspiration from a New York Times article<\/a> that showed part-to-whole percentages for the tendencies of politicians to lie or tell the truth. I liked how they highlighted the two opposing values in contrasting color while keeping the neutral value grey.<\/p>\n\n\n\n I was also inspired by other sentiment analyses<\/a> that used scatter plots to show how data points fall along a spectrum of emotion, while also using emotionally charged colors like green for pleasantness and blue for unpleasantness.<\/p>\n\n\n\n I wanted to discover which emojis were most frequently used in the collection of #MeToo tweets so I could narrow down the visuals to a more targeted set of datapoints.\u00a0 For all graphs (but the scatter plot) I filtered by the top 20 most frequently occurring emojis in the #MeToo dataset.<\/p>\n\n\n\n To contrast positive and negative sentiment, I used opposing colors and also tried to incorporate some color psychology by using green for positive sentiment and red for negative sentiment. I desaturated the colors to avoid too much eye fatigue and ran it through a color blindness simulator<\/a> to make sure it passed red\/green color blindness tests. For other graphs I chose to incorporate the shade of pink used by metoomvmt.org<\/a> to show homage to Tarana Burke, the founder of the Me-Too movement, and her organization.<\/p>\n\n\n\n The titles for each graph posed the question I was trying to answer in that visual, and I added brief captions to each visual to provide supplemental instructions when looking at the data.<\/p>\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2021\/10\/image.png?resize=461%2C242&ssl=1\")
<\/figure><\/div>\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2021\/10\/image-1.png?resize=576%2C95&ssl=1\")
<\/figure><\/div>\n\n\n\nVisual Inspiration<\/strong><\/h2>\n\n\n\n
![\"\"](\"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2021\/10\/Screen-Shot-2021-10-20-at-5.38.36-PM-1024x993.png?resize=431%2C417&ssl=1\")
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<\/figure><\/div>\n\n\n\nData Analysis and Visualization Process<\/h2>\n\n\n\n
Results<\/h2>\n\n\n\n
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<\/figure><\/div>\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2021\/10\/image-4.png?resize=496%2C480&ssl=1\")
<\/figure><\/div>\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2021\/10\/image-5.png?resize=808%2C357&ssl=1\")
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