sentiment is just a stepping stone
TRANSCRIPT
Social Media & Web Analytics InnovationSentiment is just a stepping stone
Social Media & Web Analytics Innovation
Hello online viewers of this slide deck!
• A lot of the content here is visual—you’ll want to download the full presentation and read the notes fields
• You can also (soon) find the video version by looking at the Social Media & Web Analytics Innovation site
• You can also stay tuned for more content by checking out our blog: http://www.idibon.com/blog• The case studies are, partly, covered by these blog posts btw:• http://idibon.com/toxicity-in-reddit-communities-a-journey-to-the-darkest-d
epths-of-the-interwebs/
• http://idibon.com/run-fast-as-you-can-likeagirl-advocates-and-brand-campaign-roi/
• http://idibon.com/idibon-supports-unicef-provide-natural-language-processing-sms-based-social-monitoring-systems-africa/
Social Media & Web Analytics Innovation
What’s ahead
• Quick overview of sentiment analysis• It’s tricky• And limited
• Can we do more?• Yep
• Case studies• Detecting toxicity/supportiveness of Reddit communities• Understanding the effectiveness of Always’ #LikeAGirl
campaign• Routing text messages to different groups in UNICEF
Social Media & Web Analytics Innovation
We are not robots
Social Media & Web Analytics Innovation
Though automation makes our lives easier
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
Referential
Social Media & Web Analytics Innovation
Persuasive
Social Media & Web Analytics Innovation
Expressive
Social Media & Web Analytics Innovation
How do you feel?
Social Media & Web Analytics Innovation
13 expert polarity lexiconsWords on 2 or more= 10,592 affective words
Social Media & Web Analytics Innovation
We don’t stand still
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
Yasssssss!
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
Snug as a bug in a rug
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
4 billion web pages20 million candidates1-10 words each178,104 polarity phrases
Social Media & Web Analytics Innovation
(In English)(only)
Social Media & Web Analytics Innovation
Dutch
tet
“Underscores the polarity of the clause and expresses either irritation or surprise, as if he or she had expected the opposite state of affairs”
Social Media & Web Analytics Innovation
Tongan
si’i and si’a
Different determiners (~the, that, etc) express sympathy
Social Media & Web Analytics Innovation
Cantonese
-k at the end of particles
“An emotion intensifier”
Social Media & Web Analytics Innovation
95% of the world’s conversationsare not in English
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
Different domains have different proportions
Positive
Negative
Conflict
Neutral
0% 10% 20% 30% 40% 50% 60% 70%
RestaurantsLaptops
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
“Okay, okay. Sentiment is complicated”
Social Media & Web Analytics Innovation
Real question: Can you take action?
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
How is sentiment for particular categories?
Positive
Negative
0% 10% 20% 30% 40% 50% 60% 70% 80%
AnecdotesAmbienceServicePriceFood
Social Media & Web Analytics Innovation
Setting the bar—at a minimum:Accuracy
(which is tied to your training data)+
An ability to do something
Social Media & Web Analytics Innovation
BEYOND SENTIMENT
Social Media & Web Analytics Innovation
What would you do with unlimited human analysts?
You’d ask them to classify messages into categories that enable you to take action.
Machine learning models with humans-in-the-loop can power sophisticated classification.
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
Toxicity > sentiment
• People don’t like things; they talk about them• Negative comments aren’t the same as toxic comments• Negative can be constructive
• Finding hateful and hate-inciting speech—that’s important • To keep people safe• To keep communities healthy
Social Media & Web Analytics Innovation
The importance of definition
• If people can’t agree on what’s-in and what’s-out, it’s hard to train a machine
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
Wait a sec! Aren’t these ducks?(Can we agree to disagree?)
Social Media & Web Analytics Innovation
The importance of definition
• If people can’t agree on what’s-in and what’s-out, it’s hard to train a machine
• In our case toxicity was defined as:• ad hominem attacks (directed at specific people)• bigoted comments (e.g., sexist, racist, homophobic, etc)
• Set definitions• Then see if people are consistent • Run pilots• Do inter-annotator agreement• Iterate
Social Media & Web Analytics Innovation
Sentiment is not IRRELEVANT
• A lot of comments are Neutral• So that doesn’t teach us much about hate speech• And we’ll waste a lot of time and money getting training
data on Neutral• So we ran an experiment:• Annotate random data • Annotate stuff that our sentiment models say is Negative
Social Media & Web Analytics Innovation
Work savings!
• Items chosen for review based on our sentiment model were MUCH more likely to be toxic or supportive• A decrease of 96% of effort
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
Analyst time savings is a key benefit
% analyst time saved
% accuracy (compared to humans)
73%
83%
88%
80%
91%
81%
87%
85%
90%
99%
Finding relevant business articles
News category 1 News category 2 Health sciencesNews category 4 Manufacturing
Social Media & Web Analytics Innovation
Okay back to community health
Social Media & Web Analytics Innovation
Finding healthy communities (supportive)
Social Media & Web Analytics Innovation
And unhealthy ones (toxic)
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
Unstructured data gets structured (bonus: a system that gets smarter over time)
Adaptive System
Machine Learning
Optimization
Human Annotation
Prediction Engine
Structured Data Reports
Action
Social Media & Web Analytics Innovation
By structuring text, you can do all kinds of visualizations
Social Media & Web Analytics Innovation
Learning more about ad campaigns than just “people liked it”: #LikeAGirl
Social Media & Web Analytics Innovation
The most re-shared #LikeAGirl post
Social Media & Web Analytics Innovation
60 second ad= ~ $9 million114.4 million viewers= ~ $0.08 per viewer
Social Media & Web Analytics Innovation
Always only spent 30%of what Anheuser-Busch didBut they had twice the tweets
Social Media & Web Analytics Innovation
Not all sharers and resharers are of equal value
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
Influencers extend the brand a lot
Social Media & Web Analytics Innovation
Posts by brand and ad advocates reach twice as far as posts by @Always
Social Media & Web Analytics Innovation
If we lumped everyone who used #LikeAGirl togetherWe wouldn’t know the difference betweenPeople talking about the ad (and products)
And people talking about the cause
Social Media & Web Analytics Innovation
Antagonists mainly posted their sexist content to #LikeABoyDefenders overwhelmed them with 3-4 times the content (yay!)
Social Media & Web Analytics Innovation
Positive sentiment would lump everyone togetherAnd negative sentiment would lump
Antagonists (sexists)in with
Defenders (anti-sexist)
Social Media & Web Analytics Innovation
Routing messages that matter
Social Media & Web Analytics Innovation
Processing millions of SMS in 12 African languages
Intent of sender(i.e. report a problem, ask
a question or make a suggestion)
Categorization(i.e. orphans and
vulnerable children, violence against children,
health, nutrition)
Language detection(i.e. English, Acholi,
Karamojong, Luganda, Nkole, Swahili, Lango)
Location(i.e. village names)
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
1.4%
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
Top 3 categories in Nigeria
Employment
U-report support
Health
9.69%
17.68%
39.44%
Social Media & Web Analytics Innovation
Quick conclusion
• Sentiment analysis is pretty rudimentary • On its own, it rarely answers key business questions • Though it IS automatic and scalable
• Think of it as an example of natural language processing• There’s a lot more you can do• The key is formulating specific questions• And training the system on RELEVANT data• For this, you’ll need to optimize humans
Social Media & Web Analytics Innovation
Accuracy of ~20 teams
Restaurant categories (F-score)
Restaurant category polarity (F-score)
Top score 88.57 82.92
Median 74.24 69.75
Baseline (~ “let’s always guess the most popular category)
68.89 64.09
We care about overall accuracy, so we need to multiply how often the right category goes with the right polarity.
Social Media & Web Analytics Innovation
95% of the world’s conversations are not in English. Idibon covers 99% of the world’s GDP.
Rapidly tag and filter your chosen topics and criteria in any language
Monitor how people respond to your brand differently around the world
One unified system versus data cobbled together from disparate systems
Idibon works with:
English, Portuguese (Brazilian and from Portugal), Spanish, Italian, French, Russian, German, Turkish, Arabic, Japanese, Greek, Mandarin Chinese, Persian, Polish, Dutch, Swedish, Serbian, Romanian, Korean, Hungarian, Bulgarian, Hindi, Croatian, Czech, Ukrainian, Finnish, Hebrew, Urdu, Catalan, Slovak, Indonesian, Malay, Vietnamese, Bengali, Thai, Navajo, Latvian, Estonian, Lithuanian, Kurdish, Yoruba, Amharic, Zulu, Hausa, Kazakh, Sindhi, Punjabi, Tagalog, Cebuano, Danish and Emoji.
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
Social Media & Web Analytics Innovation
Navajo
=go
Emotional evaluation in narrative