Text Mining and Marketing Analytics: BeyondOnline Consumer Reviews

Olivier Toubia

Columbia Business School

NYU Conference on Digital Big Data, October 23, 2015

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Big Text Data: Beyond Online Consumer Reviews

Product Descriptions (with Garud Iyengar, Alain Lemaire, andRenee Bunnel)

Online Search Queries (with Jia Liu)

Ideas (with Oded Netzer)

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Product Descriptions - Entertainment Products

I Model/measure individual-level preferences for entertainmentproducts?

I Taxonomy of entertainment products based on psychology ofconsumption

I Complements traditional genre classificationI Based on Positive Psychology literature: Character Strengths

I Seeded Latent Dirichlet Allocation (LDA) applied to moviedescriptions

I Recommend entertainment products that increase well-being?(future research)

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Top 20 topics (Character Strengths) from Seeded LDA

Topic Examples of seed wordswith large weights

Example of moviewith large weight

Citizenship 1 border, citizens, government ElysiumCitizenship 2 family, illegal, together In AmericaCitizenship 3 neighbor, society, together Erin BrockovichCreativity 1 music, song, talent RayCreativity 3 dance, design, paint Billy ElliotCreativity 4 masterpiece, story, writer Life of PiCuriosity 1 brain, discover, learn The Pursuit of HappynessCuriosity 2 discover, investigate, reveal Slumdog MillionaireCuriosity 3 discover, fact, intrigued Kissing Jessica SteinCuriosity 4 answers, journey, searching Yes Man

Leadership 1 coach, team, victory Shaolin SoccerLeadership 3 chairman, chief, officer Memoirs of a Geisha

Love 1 kiss, married, sex Sex and the CityLove 2 hug, mother, sister P. S. I Love YouLove 3 feeling, married, partner Under the Tuscan SunLove 4 divorced, mistress, wife Changing Lanes

Love of Learning 1 book, read, write Midnight in ParisLove of Learning 2 school, students, teacher Larry Crowne

Persistence 2 determination, fight, training Rocky BalboaVitality 4 life, peppy, sparkle Blue Jasmine

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Big Text Data: Beyond Online Consumer Reviews

Product Descriptions (with Garud Iyengar, Alain Lemaire, andRenee Bunnel)

Online Search Queries (with Jia Liu)

Ideas (with Oded Netzer)

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Online Search Queries

I Incorporate text-based search into search models?

I ”Affordable sedan made in the USA”

I Preference-based search: information needs ∼ online queryI Semantic-based search: user leverages semantic relationships

to increase query efficiency

I Identification issue → incentive-aligned ”query game”

I Consumers have the ability to leverage semantic relationshipswhen formulating queries

I Parsimonious approximation of relevant semantic relationships

I Identify systematic biases in consumers’ beliefs

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Big Text Data: Beyond Online Consumer Reviews

Product Descriptions (with Garud Iyengar, Alain Lemaire, andRenee Bunnel)

Online Search Queries (with Jia Liu)

Ideas (with Oded Netzer)

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Idea Generation, Creativity, and Prototypicality

I Use ”big data” to better understand the idea generationprocess and help people be more creative?

I Novelty vs. Familiarity:I Pick ”ingredients” (words) and combine them in new waysI Optimal balance between novelty and familiarityI Balance between novelty and familiarity captured by edge

weight distribution in semantic networkI Beauty-in-averageness effect: average distribution should be

close to optimal

I Ideas with semantic subnetworks that have a more prototypicaledge weight distribution are judged as more creative

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Idea #648: ”-LOSE a car -GET money for a worse car or repairs-GIVE money. Pay into it.”

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Idea #417: ”People can pay a charge when they buy stocks, thatwill return a portion of their investment to them in case of arecession.”

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Idea #201: ”-LOSE Job -GET A guarantee of 70% of their formersalary for 5 years if they cannot find a job that paid as much asthey were making. -GIVE A premium every month based on theirsalary.”

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THANK YOU!

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