fung sims

Text Use in Online Dating Profiles: 

Text Use in Online Dating Profiles James Fung | Christo Sims ANLP | Final Presentation Instructor Marti Hearst 12.04.06

Overview: 

Overview

Slide3: 

Goal An exploratory exercise: can we use the text someone provides in their dating profile to assign them to various classes?

Slide4: 

The Text Can't wait to get to know you Nice, warm and sweet, as most of my friends would describe me. I love to laugh all the time. I have a strong passion towards life even through little things. I tend to be quiet in a large group but generally great with one on one basis. I am ambitious about love and romance. And I am very respectful of the needs and wants of other people. Life is a beautiful journey. I am seeking someone who would appreciate the value of life, family, have a warm heart and nice peronality to share this journey with. If you are that person, I can't wait to get to know you. (female, asian, college grad)

Possible Classes: 

Possible Classes Education Gender Attend Services Income Ethnicity Marital Status Want kids Others Astrology?

Approach: 

Approach

Scraping Profiles: 

Scraping Profiles Yahoo! Personals 200 Male seeking Female 200 Female seeking Male Within 50 Miles of San Francisco Ages 25-35

Feature Extraction (Python): 

Feature Extraction (Python) Token frequency Words: TF, TF.IDF Bigrams Weighted headlines Readability measures Characters, syllables, words, complex words, sentences Ratios of the above Gunning-Fog and six others

Feature Selection & Classification (Weka): 

Feature Selection & Classification (Weka) Use Weka’s built in feature selection tools Chi-Squared, Information Gain Subset Eval (not working well with most of the classes) Explore a variety of classification algorithms, for a variety of possible classes Multinomial Naïve Bayes K-Nearest Neighbors Decision Tree Support Vector Machines

Preliminary Results: 

Preliminary Results

Preliminary Results: 

Preliminary Results Able to beat a naïve baseline in a few cases, usually where there are only two or three possible classification categories: Gender ~69% Accuracy Category # Instances Women seeking a man 196 Man seeking a woman 200 (51%) Want (more) kids ~65% Accuracy Category # Instances Yes 202 (62.3%) Not sure 105 No 17

Preliminary Results: 

Preliminary Results More difficult with more classification categories: Education 47.4% Accuracy Category # Instances Post-Graduate 94 College Grad 175 (44%) Some College 86 High School Grad 13 Some High School 3 Income (61% null reply) Employment Status (75% Full-time) Political Views Attend Services Ethnicity

Preliminary Results (cont.): 

Preliminary Results (cont.) Some interesting statistics about feature probability for a given class (from multinomial Bayes output): Gender “man” - over 2x as likely in women’s profile “sense” - over 2x as likely in woman’s profile “honest” - over 2x as likely in female profile “independent” - over 3x as likely in female profile “loving” - over 3x as likely in female profile “crazy” - over 4x as likely in male profile “company” - over 3x as likely in male profile “friendship” - over 2x as likely in female profile “me laugh” - almost 4x as likely in female profile “great sense” - over 6x as likely in female profile

Preliminary Results (cont.): 

Preliminary Results (cont.) Some interesting statistics about features (from multinomial bayes): Want (more) kids: “caring” - over 3x as likely in the “yes” than the “not sure” class “heart” - over 2x as likely in the “yes” than the “not sure” class “sometimes” - over 2x as likely in the “not sure” than the “yes” class “beautiful” - 2x as likely in “yes” than “not sure” “real” - 2x as likely in “not sure” than “yes” “dancing” - 2x as likely in the “not sure” than “yes” class “games” - almost 2x as likely in the “not sure” than “yes” class

Challenges: 

Challenges

Not Enough Instances: 

Not Enough Instances For most classes, we don’t have enough instances for meaningful training: Education: Some College 87 College Grad 176 Post-Graduate 97 High School Grad 14 Some High School 3 Ethnicity Hispanic/Latino 33 Caucasian (white) 202 Asian 74 Inter-racial 14 African American (black) 38 Other 13 Pacific Islander 7 Native American 1 East Indian 8 Middle Eastern 1

Features Aren’t Working: 

Features Aren’t Working Weka identifies few relevant features Subset Eval selects subsets of size 1-3 Difficult to overcome strong a priori probability:

Additional Challenges : 

Additional Challenges I am 33old woman from Ireland living here for a few years Love this country and love the out doors, favourite thing is mountain biking and hiking tooSan Francisco has so much to offer, nice restaurants which i love Thai food and so many live music shows which i love to out and listen every month … No punctuation

Additional Challenges (cont.): 

Additional Challenges (cont.) I am into swimming, sunrises, Vinyasa Yoga at the Loft, cafes, people watching, warm drinks in the morning, laughing, crying, feeling all of it, freshly squeezed juice, tennis, painting, spirals, Abraham Hicks, Life as Art, singing, swinging, sushi, backgammon, remembering my dreams, warm weather, soft textures, calligraphy, episopalian upbringing gone buddhist tendencies, handmade paper, dancing, fire, telling stories … Lists, not sentences

Additional Challenges (cont.): 

Additional Challenges (cont.) I work alot but in my free time i love to play a round of golf and spend time out with my dog. I love going to the beach with him or going to the park and just chillin out. At night i love goin out with friend and having a few drinks. Lack of complex words

Additional Challenges: 

Additional Challenges Scraping profiles requires a user login Easy in PHP, not in Python Have to save profiles by hand, limits corpus size The profile text in Yahoo! Personals doesn’t seem as thoughtful as profiles on Match.com Shorter profile text Spam? How dedicated are the participants?

Where we’re headed: 

Where we’re headed

Future Work (cont.): 

Future Work (cont.) Need more profiles! PHP manually saved Different features Use of capitalization: emphasis, grammar Tailored features More token features “I go to church I am very sincere in my faith and my striving to become more Like Jesus. By know means am I perfect, however, NEW MERCIES EVERYDAY! … I am a very real and straight forward person, however,HUMBLE to God's word and voice in my life. Always looking to HIM for my direction and HE is my SOURCE”