What’s Love Got To Do With It? Ruby and Sentiment Analysis

What’s Love Got To Do With It? Ruby and Sentiment Analysis @RABBIGREENBERG

Hello! • I 💙 Ruby • Former rabbi & community organizer • 🌎 San Diego ↝ NYC ↝ Boston ↝ Denver ↝ Tel Aviv 🌍 • Developer Advocate @ Nexmo @RABBIGREENBERG

Our Roadmap @RABBIGREENBERG A. B. C. D. E. Why does this matter? What is sentiment analysis? What can I build with it? Let’s build it! Test Run and Data Analysis

Why does this matter? @RABBIGREENBERG

Language is not only a passive vehicle for information @RABBIGREENBERG

“Last night I had dinner with… someone, it was delicious“ @RABBIGREENBERG

‫’’אתמול בערב אכלתי‬ ‫ארוחת ערב עם…‬ ‫מישהו ‪ ,‬היא‬ ‫טעיימה’’‬ ‫‪@RABBIGREENBERG‬‬

Egocentric or Geocentric? @RABBIGREENBERG

… if you saw a Guugu Yimithirr speaker pointing at himself, you would naturally assume he meant to draw attention to himself. In fact, he is pointing at a cardinal direction that happens to be behind his back. While we are always at the center of the world, a Guugu Yimithirr speaker points through himself… - Guy Deutscher, The New York Times, 2010 @RABBIGREENBERG

“When a language dies, a way of understanding the world dies with it, a way of looking at the world.” - George Steiner @RABBIGREENBERG

Language is a conduit constructor @RABBIGREENBERG

Understanding language helps us in understanding human society @RABBIGREENBERG

Algorithms can assist in getting us there @RABBIGREENBERG

What is Natural Language Understanding & Sentiment Analysis? @RABBIGREENBERG

“ I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted. “

• Alan Turing, 1950 @RABBIGREENBERG

“Last night I had dinner with someone, it was delicious“ @RABBIGREENBERG

Natural Language Processing @RABBIGREENBERG

Formal Grammar Inflectional Endings Part-of-speech (POS) tagging Parsing sentences Sentence boundaries Root form of words @RABBIGREENBERG

Segment continuous text into separate words (prevalent in many languages) Relationships of named entities to each other (who to whom, what to what, etc.) Topic segmentation Word meaning probability @RABBIGREENBERG

What’s my polarity? “I’d really truly “I’d really “I love my mobiletruly but love going lovenot going outout inin would recommend it thiscolleagues.” weather!” to any of my this weather!” “I dislike broken cars.” *examples from Wikipedia @RABBIGREENBERG

What can I make with it? @RABBIGREENBERG

Sometimes you just need to get to the point @RABBIGREENBERG

…and just tell me What’s the mood of the news? @RABBIGREENBERG

WHAT ARE WE BUILDING (1) “topic” (2) (3) @RABBIGREENBERG

Let’s build it @RABBIGREENBERG

What APIs will we be using? NEWS API “Search worldwide news with code” Generous free access tier Ruby SDK Documentation www.newsapi.org IBM WATSON Natural Language Analyzer API Generous free access tier Ruby SDK Documentation www.ibm.com/watson/developer NEXMO Messages API Generous free access tier Ruby SDK Documentation developer.nexmo.com @RABBIGREENBERG

Gemfile @RABBIGREENBERG

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Routes @RABBIGREENBERG

@RABBIGREENBERG

Environment Variables @RABBIGREENBERG

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Controller @RABBIGREENBERG

Defining Methods @rabbigreenberg @RABBIGREENBERG

#inbound @RABBIGREENBERG

#analyze_headlines @RABBIGREENBERG

#get_news_headlines @RABBIGREENBERG

_ p p a s t a h w _ d n #se msg @RABBIGREENBERG

#generate_jwt_token @RABBIGREENBERG

Giving It A Go @RABBIGREENBERG

@RABBIGREENBERG

Examining the Data @RABBIGREENBERG

What were the headlines? @RABBIGREENBERG IBM, Microsoft, a medley of others sing support for Google against Oracle in Supremes Java API copyright case Future-proof your tech skills with the Ultimate Deep Learning Bundle An Illuminated Dress Made With FLORA and Neopixels from The Dressed Aesthetic #WearableWednesday @dressed_app George Cheeks Exits As Vice Chairman of NBCUniversal Content Studios To win in a shifting market, the streaming industry is bundling up How CollegeHumor lost its early lead The Rundown: Video metrics will never be totally reliable 台積電 5 奈奈⽶米即將量量產，可望獨拿蘋果、華為訂單 Sequential’s Pro 3 is a new Prophet, while the others clone – so how does it stack up? The Continental: John Wick TV Show Is ‘Moving Along,’ Says Starz Boss iOS 13.3.1 Public Beta 2 Is Back After A Long Hiatus Perfect Sense unveils Gyro to simplify cloud infrastructure management “Stargirl” Is Ready to Embrace Her Destiny – But Will She Survive The Passing of The Cosmic Staff? [TRAILER] “Crisis” Management: Yup, Grant Gustin’s The Flash Just Met [SPOILER] – Snyder Cut of “Crisis” to Follow? “Crisis” Management: What’s A Little “Universal Domination” Among Enemies – Right, Lex? [PREVIEW] Labbers of the World Unite to Write a Book in 1 Week Through a Book Sprint Instagram Makes Long-Lived Access Tokens Available for the Instagram Basic Display API Aaron Rodgers jokes he’ll ‘definitely’ shake Richard Sherman’s hand 49ers’ turnaround, win improvement has put team in exclusive company NFL rumors: 49ers’ Joe Woods finalist for Browns defensive coordinator

In Numbers 18 Headlines in Sample 7 Programming Related 38.8% of Sample Directly Related to Topic @RABBIGREENBERG

In other words… Always examine your data. @RABBIGREENBERG

Examining the Analysis @RABBIGREENBERG

What were the sentiment results? Score: -0.62093 Label: negative @RABBIGREENBERG

What were the emotion results? @RABBIGREENBERG

Why the discrepancy? @RABBIGREENBERG

To understand we return to the data What were the concepts identified? @RABBIGREENBERG

Concept Scoring Concept Relevance Board of Directors 90% Richard Sherman 87% Chairman 84% Application Programming Interface 83% @RABBIGREENBERG

Where does this leave us? @RABBIGREENBERG

“ I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted. “

• Alan Turing, 1950 @RABBIGREENBERG

Thank you! @RABBIGREENBERG

Resources • • • • • • Deutscher, G. (2010). Through the Language Glass: Why The World Looks Different in Other Languages, Metropolitan Books Giannakidou, A. (2011). Positive polarity items and negative polarity items: variation, licensing, and compositionality, University of Chicago IBM Watson Natural Language Understanding API Reference: https://cloud.ibm.com/apidocs/natural-language-understanding/ natural-language-understanding Nexmo Messages API Reference: https://developer.nexmo.com/api/messages-olympus News API Reference: https://newsapi.org/docs “Mood of the News” GitHub Repository: https://github.com/Nexmo/mood-of-the-news @RABBIGREENBERG