joseph ‘jofish’ kaye info.634 1 december 2004

16
Horoscope Classification Using Support Vector Machines Part of the Home Health Horoscopes Project Joseph ‘Jofish’ Kaye INFO.634 1 December 2004

Upload: rae-deleon

Post on 31-Dec-2015

26 views

Category:

Documents


0 download

DESCRIPTION

Horoscope Classification Using Support Vector Machines Part of the Home Health Horoscopes Project. Joseph ‘Jofish’ Kaye INFO.634 1 December 2004. Full HHH Project. For this project, I’m looking at creating a sorted horoscope database for use in horoscope generation. Background. - PowerPoint PPT Presentation

TRANSCRIPT

Page 1: Joseph ‘Jofish’ Kaye INFO.634 1 December 2004

Horoscope Classification Using Support Vector

Machines

Part of the Home Health Horoscopes

Project

Joseph ‘Jofish’ KayeINFO.634

1 December 2004

Page 2: Joseph ‘Jofish’ Kaye INFO.634 1 December 2004

Full HHH Project

For this project, I’m looking at creating a sorted horoscope database for use in horoscope generation.

Page 3: Joseph ‘Jofish’ Kaye INFO.634 1 December 2004

Background

• Home Health Horoscopes: – collaboration with

• Phoebe Sengers (Cornell)• Bill Gaver (Royal College of Art, London)m

Page 4: Joseph ‘Jofish’ Kaye INFO.634 1 December 2004

The problem with Ubiquitous & Context Aware Computing

Current technology rhetoric around ubiquitous computing posits a legion of smart sensors deriving your every activity in its full complexity as a function of their combined outputs. The reality is that current sensor and artificial intelligence systems cannot come close to capturing the complexity of human action, even in a limited domain, such as the home.

Page 5: Joseph ‘Jofish’ Kaye INFO.634 1 December 2004

HHH: A probe for discussion

By using horoscopes as the final output of a ubiquitous computing system in a home, we are able to allow for human complexities and recognize the ambiguity inherent in the contexts derived from our sensor inputs.

Page 6: Joseph ‘Jofish’ Kaye INFO.634 1 December 2004

Previous work: Psychology

• Forer 1949: The Fallacy of Personal Validation: A Classroom Demonstration of Gullibility.

• “Security is one of your major goals in life.”• “Some of your aspirations tend to be pretty

unrealistic.”

• Snyder 1974: Why Horoscopes Are True: The Effects Of Specificity On Acceptance Of Astrological Interpretations

• Similar statements to above rated 3.24/5 if told the statements were "generally true of people", 3.76/5 if they were based on the subject's year and month of birth and 4.38/5 if based on year, month and day of birth.

Page 7: Joseph ‘Jofish’ Kaye INFO.634 1 December 2004

Previous work: Critical Theory

• Adorno 1953: The Stars Down to Earth– Horoscopes reinforce existing social

structures – Pseudo-individualization

• Display that keen mind of yours“• "Follow up on that intuition of yours".

– Vice presidential level– “Bi-phasic approach”:

• “The problem with how to dispense with the contradictory requirements of life is solved by the simple device of distributing these requirements over different periods, mostly of the same day.”

Page 8: Joseph ‘Jofish’ Kaye INFO.634 1 December 2004

Raw Materials

• 45,000 unlabelled horoscopes screen-scraped from the web, split into 180,000 sentences.– Average sentence (“document”) length 11.9

words.

• 900 labeled horoscopes in six categories scraped from a fourth website, split into 1500 sentences.– Average sentence (“document”) length 9.3 words.

• Six categories: career, economy, health, luck, love, and relations.

Page 9: Joseph ‘Jofish’ Kaye INFO.634 1 December 2004

Progressive ratios of test:train to determine accuracy

Page 10: Joseph ‘Jofish’ Kaye INFO.634 1 December 2004

Use models to label corpus• Modify b parameter to maximize

accuracy• Check by using 100% test data to

check 100% training data• Apply to entire unlabelled corpus to

make list of sentences that are ~certain of being about luck

Page 11: Joseph ‘Jofish’ Kaye INFO.634 1 December 2004

Sample: Luck class

• 175063 original sentences• Apply luck model:

– 562 support vectors– Maximum accuracy at b=0.44 for 96.9%

• 168694 sentences rated negatively in luck class

• 6394 sentences that are definitely about luck

Page 12: Joseph ‘Jofish’ Kaye INFO.634 1 December 2004

Distinctly luck-class sentences:Top ten sentences most likely to be not about luck:

• 3.1022525 Good luck.• 2.2188252 Luck is with you.• 2.1527156 Lady luck is with you.• 2.1347538 You'll seek fortune through

investments and a little good luck.• 2.0986433 Don't push your luck.• 2.0925958 An apparent stroke of fortune is really

the culmination of good luck and hard labor.• 2.0715444 A potentially luck filled day.• 1.9987403 Take advantage of your good luck.• 1.9326159 Go ahead...press your luck button.• 1.893451 Luck should swing your way today.• 1.8281272 A sense of impending good fortune

motivates you today, so press your luck to the limit.

Page 13: Joseph ‘Jofish’ Kaye INFO.634 1 December 2004

Definitively not about luckTop ten sentences most likely to be not about luck:• -1.4375058 Love and romance will be on your mind.• -1.4413726 Then find someone else and do it up right.• -1.4429443 Look into making changes that will help you

look and feel better about yourself.• -1.443887 Adopting a positive attitude will help you

relax and enjoy better health.• -1.451634 Your mind is saying work work work but your

heart is saying fun excitement and romance - watch which one you listen to.

• -1.4528898 Your focus should be on your work and what you can do to make it better.

• -1.4834784 Work for a cause other than yourself.• -1.4886628 It will all work out.• -1.4990723 Work on yourself.• -1.5355008 Support will work better than criticism.• -1.5617689 New and unusual work methods will

make it difficult for you to spend quality time at home.

Page 14: Joseph ‘Jofish’ Kaye INFO.634 1 December 2004

Proposal

• Goal I: Determine if it is feasible to represent horoscopes or portions thereof in a support vector machine.

• Goal II: Determine if using latent semantic indexing helps in achieving Goal I.

• Goal III. Determine if it is possible to generate horoscopes from fragments (paragraphs or sentences, depending on the inputs) of horoscopes in a SVM; else determine if it is possible pick the appropriate horoscope from a set.

Page 15: Joseph ‘Jofish’ Kaye INFO.634 1 December 2004

Project

• Goal I: Determine if it is feasible to represent horoscopes or portions thereof in a support vector machine. Done.

• Goal II: Determine if using latent semantic indexing helps in achieving Goal I. Not necessary!

• Goal III. Determine if it is possible to generate horoscopes from fragments (paragraphs or sentences, depending on the inputs) of horoscopes in a SVM; else determine if it is possible pick the appropriate horoscope from a set. 2nd part done; 1st part still possible

Page 16: Joseph ‘Jofish’ Kaye INFO.634 1 December 2004

Future work

• Combining horoscope sentences to make realistic horoscopes

• Orthogonal, attitudinal categories:– Good luck / bad luck– Increasing / decreasing– etc.

• Hooking up to sensors and• Putting into people’s houses…