Embed Size (px)
DESCRIPTION
Matthew Russell's "Mining the Geo Needles in the Social Haystack" from Where 2.0 (April, 19, 2011 - Santa Clara, CA)
Citation preview
Mining the Geo Needles in the Social Haystack
(Where 2.0, 2011)
Matthew A. Russellhttp://linkedin.com/in/ptwobrussell@ptwobrussell
About Me
2
•VP of Engineering @ Digital Reasoning Systems
•Principal @ Zaffra
•Author of Mining the Social Web et al.
•Triathlete-in-training
@SocialWebMining
Objectives
3
•Orientation to geo data in the social web space
•Hands-on exercises for analyzing/visualizing geo data
•Whet your appetite and send you away motivated and with useful
tools/insight
Approximate Schedule
•Microformats: 10 minutes
•Twitter : 15 minutes
•LinkedIn: 15 minutes
•Facebook: 15 minutes
•Text-mining: 15 minutes
•General Q&A (time-permitting)
4
Development
•Your local machine
•Python version 2.{6,7}
•Recommend Windows users try ActivePython
•We'll handle the rest along the way
5
Agile Data Solutions
Microformats
Microformats
•My definition: "conventions for unambiguously including structured
data into web pages in an entirely value-added way" (MTSW, p19)
•Bookmark and browse: http://microformats.org
•Examples:
•geo, hCard, hEvent, hResume, XFN
7
geo
8
<!-- Download MTSW pp 30-34 from XXX -->
<!-- The multiple class approach --> <span style="display: none" class="geo"> <span class="latitude">36.166</span> <span class="longitude">-86.784</span> </span>
<!-- When used as one class, the separator must be a semicolon --> <span style="display: none" class="geo">36.166; -86.784</span>
Exercise!
•View source @ http://en.wikipedia.org/wiki/List_of_U.S._national_parks
•Use http://microform.at to extract the geo data as KML
•http://microform.at/?type=geo&url=http%3A%2F%2Fen.wikipedia.org
%2Fwiki%2FList_of_U.S._national_parks
•Try pasting this URL into Google Maps and see what happens
9
10
Exercise Results
• Feel free to hack on the KML
• http://code.google.com/apis/kml/documentation/
• Google Earth can be fun too
• But you already knew that
• We'll see it later...
Agile Data Solutions
Twitter Data
12
•There's geo data in the user profile
•And in tweets...
• ...if the user enabled it in their prefs
•And even in the 140 chars of the tweet itself
A Tweet as JSON
13
{ "user" : { "name" : "Matthew Russell", "description" : "Author of Mining the Social Web; International Sex Symbol", "location" : "Franklin, TN", "screen_name" : "ptwobrussell", ... }, "geo" : { "type" : "Point", "coordinates" : [36.166, 86.784]}, "text" : "Franklin, TN is the best small town in the whole wide world #WIN", ...}
Exercise!
14
http://api.twitter.com/1/users/show.json?screen_name=ptwobrussell
$ sudo easy_install twitter # 1.6.1 is the current$ python>>> import twitter>>> t = twitter.Twitter()>>> user = t.users.show(screen_name='ptwobrussell')>>> import json>>> print json.dumps(user, indent=2)
•In your browser, try accessing this URL:
• In a terminal with Python, try it programatically:
Recipe #21
•Geocode locations in profiles:
•https://github.com/ptwobrussell/Recipes-for-Mining-Twitter/blob/
master/recipe__geocode_profile_locations.py
•Recipe #21 from 21 Recipes for Mining Twitter
15
Sample Results
16
<?xml version="1.0" encoding="UTF-8"?> <kml xmlns="http://earth.google.com/kml/2.0"> <Folder> <name>Geocoded profiles for Twitterers showing up in search results for ... </name> <Placemark> <Style> <LineStyle> <color>cc0000ff</color> <width>5.0</width> </LineStyle> </Style> <name>Paris</name> <Point> <coordinates>2.3509871,48.8566667,0</coordinates> </Point> </Placemark> ... </kml>
Recipe #20
•Visualizing results with a Dorling Cartogram:
•https://github.com/ptwobrussell/Recipes-for-Mining-Twitter/blob/
master/recipe__dorling_cartogram.py
•Recipe #20 from 21 Recipes for Mining Twitter
17
18
Sample Results
Recipe #22 (?!?)
19
•Extracting "geo" fields from a batch of search results
•https://github.com/ptwobrussell/Recipes-for-Mining-Twitter/blob/
master/recipe__geocode_tweets.py
•Not in current edition of 21 Recipes for Mining Twitter
•Just checked in especially for you
Sample Results
20
[None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, {u'type': u'Point', u'coordinates': [32.802900000000001, -96.828100000000006]}, {u'type': u'Point', u'coordinates': [33.793300000000002, -117.852]}, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, {u'type': u'Point', u'coordinates': [35.512099999999997, -97.631299999999996]}, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]
•Unfortunately (???), "geo" data for
tweets seems really scarce
•Varies according to a particular
user's privacy mindset?
•Examining only Twitter users who
enable "geo" would be interesting
in and of itself
Mining the 140 Characters
•Not a trivial exercise
•Mining natural language data is hard
•Mining bastardized natural language data is even harder
•We'll look at mining natural language data later
21
Fun Possibilities
22
#TeaParty#JustinBieber
Oh, and by the way...
23
OAuth 1.0a - Nowimport twitterfrom twitter.oauth_dance import oauth_dance
# Get these from http://dev.twitter.com/apps/newconsumer_key, consumer_secret = 'key', 'secret'
(oauth_token, oauth_token_secret) = oauth_dance('MiningTheSocialWeb', consumer_key, consumer_secret)
auth=twitter.oauth.OAuth(oauth_token, oauth_token_secret, consumer_key, consumer_secret)
t = twitter.Twitter(domain='api.twitter.com', auth=auth)
OAuth 2.0 - "Soon" +----------+ Client Identifier +---------------+ | -+----(A)--- & Redirect URI ------>| | | End-user | | Authorization | | at |<---(B)-- User authenticates --->| Server | | Browser | | | | -+----(C)-- Authorization Code ---<| | +-|----|---+ +---------------+ | | ^ v (A) (C) | | | | | | ^ v | | +---------+ | | | |>---(D)-- Client Credentials, --------' | | Web | Authorization Code, | | Client | & Redirect URI | | | | | |<---(E)----- Access Token -------------------' +---------+ (w/ Optional Refresh Token)
See http://tools.ietf.org/html/draft-ietf-oauth-v2-10#section-1.4.1
Agile Data Solutions
LinkedIn Data
27
•Coarsely grained geo data is available in user profiles
•"Greater Nashville Area", "San Francisco Bay", etc.
•Most geocoders don't seem to recognize these names...
•No geocoordinates! (Yet???)
•Mitigation approach: (1) transform/normalize and then (2) geocode
Exercise!
•Get an API key at http://code.google.com/apis/maps/signup.html
28
$ easy_install geopy$ python>>> import geopy>>> g = geopy.geocoders.Google(GOOGLE_MAPS_API_KEY)>>> results = g.geocode("Nashville", exactly_one=False)>>> for r in results:. . . print r # (u'Nashville, TN, USA', (36.165889, -86.784443))
•See also https://github.com/ptwobrussell/Recipes-for-Mining-Twitter/blob/
master/etc/geocoding_pattern.py
Diving Deeper
•Example 6-14 from MTSW (pp194-195) works though an extended example
and dumps KML output that includes clustered output
•See http://github.com/ptwobrussell/Mining-the-Social-Web/python_code/
linkedin__geocode.py
29
Clustering
•First half of MTSW Chapter 6 (pp167-188) provides a good/detailed intro
•Think of clustering as "approximate matching"
•The task of grouping items together according to a similarity metric
• It's among the most useful algorithmic techniques in all of data mining
•The catch: It's a hard problem.
•What do you name the clusters once you've created them?
30
Example Output
31
Better Data Exploration
32
Clustering Approaches
•Agglomerative (hierarchical)
•Greedy
•Approximate
•k-means
33
k-Means Algorithm
34
1. Randomly pick k points in the data space as initial values that will be used to compute the k clusters: K1, K2, ..., Kk.
2. Assign each of the n points to a cluster by finding the nearest Kn—effectively creating k clusters and requiring k*n comparisons.
3. For each of the k clusters, calculate the centroid (the mean of the cluster) and reassign its Ki value to be that value. (Hence, you’re computing “k-means” during each iteration of the algorithm.)
4. Repeat steps 2–3 until the members of the clusters do not change between iterations. Generally speaking, relatively few iterations are required for convergence.
Let's try it: http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/AppletKM.html
Step 0 (init)
35
Step 1
36
Step 2
37
Step 3
38
Step 4
39
Step 5
40
Step 6
41
Step 7
42
Step 8
43
Step 9 (done)
44
k-Means Applied
45
Agile Data Solutions
Facebook Data
47
•Ridiculous amounts of data (all kinds) is available via the FB Platform
•Current location, hometown, "checkins"
•Access to the FB platform data is relatively painless:
•Social Graph: http://developers.facebook.com/docs/reference/api/
•FQL: http://developers.facebook.com/docs/reference/fql/
FQL Checkins
•See http://developers.facebook.com/docs/reference/fql/checkin/
48
FQL Connections
•See http://developers.facebook.com/docs/reference/fql/connection/
49
Sample FQL
•An excerpt from MTSW Example 9-18 (pp306-308) conveys the gist:
50
fql = FQL(ACCESS_TOKEN)
q= \ """select name, current_location, hometown_location from user where uid in (select target_id from connection where source_id = me() and target_type = 'user')"""
results = fql.query(q)
Example "App"
•Basic idea is simple
•You already have the tools to
geocode and plot on a map...
•See also: http://answers.oreilly.com/
topic/2555-a-data-driven-game-
using-facebook-data/51
FB Platform Demo
•Mininal sample app at http://miningthesocialweb.appspot.com
•Source is at http://github.com/ptwobrussell/Mining-the-Social-Web/
web_code/facebook_gae_demo_app
52
Agile Data Solutions
Text Mining
References
54
•MTSW Chapter 7 (Google Buzz: TF-IDF, Cosine Similarity, and Collocations)
•MTSW Chapter 8 (Blogs et al.: Natural Language Processing and Beyond)
"Legacy" NLP
55
•"Legacy" => Classic Information Retrieval (IR) techniques
•Often (but not always) uses a "bag of words" model
•tf-idf metric is usually the root of the core strategy
•Variations on cosine similarity are often the fruition
•Additional higher order analytics are possible, but inevitably
cannot be optimal for deep semantic analysis
•Virtually every A-list search engine has started here
A Vector Space
56
How might you discover locations from text using "legacy" techniques?
57
Some possibilities
58
•Combinations of language dependent "hacks"•n-gram detection/examination
•bigrams, trigrams, etc.•"Proper Case" hints
•"Chipotle Mexican Grill"•prepositional phrase cues
•"in the garden", "at the store"•Gazetteers
•lists of "well-known" locations like "Statue of Liberty"
"Modern" NLP Pipeline
59
•A deeper "understanding" the data is much harder•End of Sentence (EOS) Detection •Tokenization•Part-of-Speech Tagging•Chunking•Anaphora Resolution•Extraction•Entity Resolution
•Blending in "legacy" IR techniques can be very helpful in reducing noise
Entity Interactions
60
Quality Metrics
61
•Precision = TP/(TP+FP)
•Recall = TP/(TP+FN)
•F1 = (2*P*R)/(P+R)
Exercise!•Get a webpage:
•curl http://example.com/foo.html
•Extract the text:
•curl -d @foo.html "http://www.datasciencetoolkit.org/html2story" > foo.json
•Extract the locations:
•curl -d @foo.json "http://www.datasciencetoolkit.org/text2places"
•NOTE: Windows users can work directly at http://www.datasciencetoolkit.org
62
Tools to Investigate
•NLTK - http://nltk.org
•Data Science Toolkit - http://www.datasciencetoolkit.org
•WordNet - http://wordnet.princeton.edu/
63
Agile Data Solutions
Q&A
Agile Data Solutions
The End
