1. Flickr Tag Analysis Ahmet Iscen

2. Outline Social Media What is Flickr? Flickr Photos Association Rule Latent Semantic Analysis Latent Dirichlet Allocation Conclusions

3. Social Media Important part of our daily lives today Twitter 12th largest country in the world Two new members sign up every second to LinkedIn

4. What is Flickr? Image and video hosting Acquired by Yahoo! in 2005 51 million registered members and 80 million unique visitors as of June 2011 6 million photos Widely used by researchers

5. Flickr

6. Dataset Xirong Li's Flickr-3.5M Dataset 3,500,000 images 570,000 unique tags 270,000 unique user-ids Randomly selected 250,000 images with their tags http://staff.science.uva.nl/~xirong/index.php?n=DataSet.Flic kr3m

7. Challenges Tags totally depend on the user Can be extremely noisy Huge range of possible words Examples: milos tasic milosevrodjendan verjaardagmilos desember 2005 tmo

8. Preprocessing Eliminate stopwords (a,for,the etc.) Eliminate extreme words (those that appear less than 20 photos and more than 80% of the photos. Porter Stemmer (only for association rule) Convert everything to lowercase Eliminate tags with less than 2 letters and more than 20 letters Eliminate numerical tags

9. Association Rules Mining Rapid Miner [york] --> [new] (confidence: 0.910) Support: 0.04 [geolat, geolon] --> [geotag] (confidence: 0.986) Support: 0.03 [hors, lotharlez] --> [caballo, cheval, hestur] (confidence: 0.846) Support: 0.03 [paard] --> [hors, lotharlenz, zirg] (confidence: 0.802) Support: 0.03 [hors, paard] --> [lotharlenz, zirg] (confidence: 0.802) Support: 0.03

10. Association Rules Mining Poor results. Probably due to noise and variance in data. Takes too much time to process the words and find rules. Need find alternative methods

11. Latent Semantic Analysis Same as LSI (LSI used in IR field) SVD on document-term matrix to reduce dimensionality Words are compared by taking the cosine of the angle between two vectors by any two rows.

12. Implementation Gensim – topic modeling toolkit Python Tested different corpus and topic sizes

13. Latent Semantic Analysis 250000 photos, 20 topics topic #0: 0.997*"wedding" + 0.047*"family" + 0.023*"friends" + 0.022*"party" + 0.019*"reception" + 0.013*"california" + 0.011*"ceremony" + 0.009*"india" + 0.008*"church" + 0.008*"sanfrancisco" topic #11: 0.491*"newyork" + -0.463*"china" + 0.448*"nyc" + -0.233*"beach" + 0.174*"newyorkcity" + 0.146*"italy" + -0.132*"friends" + -0.123*"flowers" + 0.119*"new" + -0.117*"beijing" topic #4: 0.586*"paris" + -0.524*"family" + 0.417*"france" + 0.186*"london" + 0.178*"party" + -0.169*"halloween" + 0.156*"europe" + -0.121*"japan" + 0.103*"travel" + 0.063*"birthday" topic #1: 0.701*"halloween" + 0.588*"party" + 0.169*"friends" + 0.165*"family" + 0.157*"birthday" + 0.126*"japan" + 0.071*"christmas" + 0.059*"london" + 0.058*"travel" + 0.055*"beach "

14. Latent Semantic Analysis 250000 photos, 50 topics topic #10: -0.655*"friends" + 0.633*"china" + 0.221*"travel" + 0.166*"beijing" + 0.136*"party" + -0.088*"beach" + 0.075*"vacation" + 0.071*"greatwall" + 0.070*"shanghai" + -0.066*"flowers" topic #28: -0.580*"india" + -0.323*"trip" + 0.279*"nature" + 0.262*"snow" + - 0.258*"dog" + -0.224*"sunset" + 0.200*"winter". topic #20: -0.527*"cat" + 0.511*"sunset" + 0.266*"sky" + -0.242*"california" + - 0.209*"sanfrancisco" + 0.198*"clouds" + -0.167*"beach" + -0.156*"flower" + - 0.149*"cats" + -0.132*"dog" topic #17: -0.323*"california" + -0.272*"sanfrancisco" + 0.269*"cat" + 0.254*"horse" + 0.211*"pferd" + 0.207*"cheval" + 0.205*"caballo" + 0.205*"paard" + 0.204*"hest" + 0.204*"cavalo"

15. Latent Semantic Analysis 250000 photos, 100 topics topic #29: 0.689*"australia" + 0.279*"sydney" + -0.233*"nature" + 0.220*"trip" + - 0.209*"france" + -0.187*"india" + -0.175*"snow" + 0.157*"new" + 0.144*"paris" + - 0.134*"winter" topic #58: 0.401*"geotagged" + 0.385*"geolat" + 0.380*"geolon" + -0.261*"people" + 0.259*"day" + 0.198*"england" + 0.191*"newzealand" + -0.178*"canada" + 0.168*"water" + -0.144*"portrait". topic #45: 0.406*"fall" + 0.398*"park" + 0.315*"october" + -0.291*"animals" + 0.289*"autumn" + -0.262*"art" + 0.182*"leaves" + -0.175*"zoo" + -0.163*"sky" + 0.132*"garden" topic #85: -0.673*"hongkong" + 0.221*"florida" + 0.221*"singapore" + 0.209*"winter" + 0.174*"museum" + -0.170*"boston" + -0.165*"scotland" + -0.153*"prague" + 0.153*"cats" + -0.136*"island"

16. Latent Semantic Analysis Notice the negative weights. Hard to interpret Probabilistic methods are not used

17. Latent Dirichlet Allocation Expectation- Maximization Each document is a mixture of topics Find the posterior for topics in the E-Step p(topic t | document d) Then update the assignment of the current word in the M-Step p(word w | topic t)

18. Latent Dirichlet Allocation 250000 photos, 20 topics topic #13: 0.088*party + 0.072*halloween + 0.027*lake + 0.024*boat + 0.022*home + 0.019*park + 0.018*river + 0.016*ice + 0.015*spring + 0.014*birthday topic #3: 0.046*trip + 0.044*vacation + 0.044*sanfrancisco + 0.040*california + 0.026*road + 0.024*cats + 0.018*school + 0.018*cruise + 0.014*ca + 0.014*old topic #8: 0.051*paris + 0.042*france + 0.027*july + 0.027*4th + 0.025*music + 0.022*car + 0.021*rock + 0.020*dogs + 0.020*concert + 0.016*geotagged

19. Latent Dirichlet Allocation 250000 photos, 50 topics topic #7: 0.111*sunset + 0.108*beach + 0.089*holiday + 0.047*fun + 0.029*smile + 0.028*forest + 0.023*rose + 0.020*wood + 0.019*disneyland + 0.019*costarica topic #14: 0.141*vacation + 0.046*san + 0.037*francisco + 0.034*sports + 0.020*hockey + 0.020*top + 0.019*cake + 0.014*cafe + 0.013*biking + 0.013*ruins topic #23: 0.112*trip + 0.070*bridge + 0.057*road + 0.048*blue + 0.048*building + 0.042*film + 0.035*orange + 0.022*university + 0.021*telephone + 0.018*sky topic #29: 0.124*party + 0.110*friends + 0.085*christmas + 0.045*rock + 0.038*lake + 0.038*ireland + 0.031*castle + 0.026*africa + 0.025*live + 0.025*music

20. Latent Dirichlet Allocation 250000 photos, 100 topics topic #10: 0.109*hawaii + 0.093*island + 0.060*la + 0.030*photoshop + 0.027*walk + 0.026*hdr + 0.024*maui + 0.023*us + 0.019*fountain + 0.018*beach topic #24: 0.172*house + 0.106*architecture + 0.077*festival + 0.068*airplane + 0.038*flying + 0.029*flight + 0.026*air + 0.025*aircraft + 0.021*aviation + 0.020*airshow topic #34: 0.231*vacation + 0.159*trip + 0.136*lake + 0.095*florida + 0.088*birds + 0.062*san + 0.051*francisco + 0.015*yellowstone + 0.015*kayak + 0.015*maltay topic #70: 0.114*november + 0.074*thanksgiving + 0.050*soccer + 0.048*polarbear + 0.048*ski + 0.041*basketball + 0.035*safari + 0.034*bear + 0.023*wien + 0.021*flood

21. Conclusions LSA and LDA are more useful for analyzing tags than Association Rule Mining There is no “best” number of topics Human interpretation still might be required

22. Future Works Increase the corpus size to 1000000 documents Analyze Flickr groups as well