Deriving Conversational Insight by Learning Emoji Representation by Jeff Weintraub
Deriving Conversational Insight by Learning Emoji Representation by Jeff Weintraub
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.
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Deriving Conversational Insight by Learning Emoji Representation by Jeff Weintraub
- 1. Deriving Conversational Insight by Learning Emoji Representations VP, Technology Jeff Weintraub a You & Mr Jones company
- 2. //BigDataLA2017 AGENDA 1. Emoji Adop?on 2. Emojineering 3. Conversa?onal Insight
- 3. Product & Technology Development1. Emoji Adoption
- 4. 4 //BigDataLA2017 Emoji Adoption -‐ Instagram October 2011 Emoji keyboard launches on iOS 10% Instagram Comments contained emoji (Nov 2011) 50%+ Instagram Comments contained emoji (March 2015) See https://engineering.instagram.com/emojineering-‐part-‐1-‐machine-‐learning-‐for-‐emoji-‐trendsmachine-‐learning-‐for-‐emoji-‐trends-‐7f5f9cb979ad
- 5. 5 //BigDataLA2017 Emoji Adoption -‐ Instagram 2,666 Emojis in Unicode Standard as of May 2017 -‐0.93 Correla?on coefficient within respec?ve cohorts See https://engineering.instagram.com/emojineering-‐part-‐1-‐machine-‐learning-‐for-‐emoji-‐trendsmachine-‐learning-‐for-‐emoji-‐trends-‐7f5f9cb979ad
- 6. Product & Technology Development2. Emojineering
- 7. 7 //BigDataLA2017 Emojineering Ford GTs are the Ford GTs are ! !
- 8. 8 //BigDataLA2017 Emojineering Ford GTs are the Ford GTs are ! ! (Pos) (Neg)
- 9. 9 //BigDataLA2017 Emojineering NLP SemanCc Analysis -‐ N-‐gram Nueral Network Language Model (NNLM) See Mikolov, et al. Efficient Estimation of Word Representations in Vector Space, 2013 Q = Training Complexity; Goal is to minimize so can be trained efficiently on more data C is the maximum distance of the words. V is size of the vocabulary; output layer dimensionality -‐ Trained with stochas?c gradient descent (SGD) and back propaga?on -‐ Maximize classifica?on of a word based on another word in the same sentence. ConCnuous Skip-‐gram Model
- 10. 10 //BigDataLA2017 Emojineering Skip-‐gram Model -‐ if we choose C = 5, for each training word we will select randomly a number R in range < 1; C >, and then use R words from history and R words from the future of the current word as correct labels. See Mikolov, et al. Efficient Estimation of Word Representations in Vector Space, 2013 -‐ increasing the range improves quality of the resul?ng word vectors, but it also increases the computa?onal complexity
- 11. 11 //BigDataLA2017 Emojineering DistribuConal Hypothesis Words that occur in similar contexts tend to have similar meanings (Harris, 1954; Firth, 1957; Deerwester et al., 1990) Training Accuracy -‐ 300 dimensional vectors; words and emojis -‐ 3 million phrases -‐ 6B tokens the, Ford, GT cars, Ford, :) See https://engineering.instagram.com/emojineering-‐part-‐1-‐machine-‐learning-‐for-‐emoji-‐trendsmachine-‐learning-‐for-‐emoji-‐trends-‐7f5f9cb979ad
- 12. 12 //BigDataLA2017 Emojineering -‐ Visualization the, Ford, GT cars, Ford, :)
- 13. 13 //BigDataLA2017 Emojineering DistribuConal Hypothesis Words that occur in similar contexts tend to have similar meanings (Harris, 1954; Firth, 1957; Deerwester et al., 1990) 100 Billion Words Model contains 300 dimensional vectors for 3 million words and phrases the, Ford, GT cars, Ford, :) 3. Conversational Insight
- 14. 14 //BigDataLA2017 Conversational Insight -‐ Entertainment Vertical 65.23% of Emojis used were Top 10 Emojis 34.7% of Emojis uses were and 😂 😍 30.01% of Emojis used were seman?cally relevant to key words
- 15. 15 //BigDataLA2017 Conversational Insight -‐ Retail Vertical 58.14% of Emojis used were Top 10 Emojis 22.5% of Emojis uses were and 😍 11.78% of Emojis used were seman?cally relevant to key words ❤
- 16. 16 //BigDataLA2017 Conversational Insight -‐ Beauty Vertical 71.22% of Emojis used were Top 10 Emojis 37.8% of Emojis uses were and 😂 😍 4% of Emojis used were seman?cally relevant to key words
- 17. //BigDataLA2017 AGENDA 1. Emoji Adop?on 2. Emojineering 3. Conversa?onal Insight
- 18. Thank You! jeff@theamplify.com @jeff_weintraub a You & Mr Jones company