The document discusses using sentiment analysis on tweets to predict time series data like stock markets or box office success. It involves 3 parts: 1) classifying tweet sentiment, 2) building a network of Twitter users, and 3) finding a time series of sentiment for each user. Methods discussed include classifying tweets as spam/not spam and objective/subjective/positive/negative, using an algorithm to extract opinion words and targets, and using community detection and LDA to analyze the Twitter user network. The end goal is to use the sentiment time series to predict real-world time series data.

1. Sentiment Analysis
1. Discover a niche network of Twitter users
2. Model their emotions on topics
3. Use feelings to more accurately predict a
time series e.g. The stock market
e.g. Box office success
4. Are some [users/networks] more influential
than others?

2. This Talk
 The Design Decision
 The Core Goals
 The 3 parts of the project:
1. Classifying the SENTIMENT of tweets
2. Building a NETWORK of twitter users
3. Finding a TIME SERIES of sentiment for each
user

3. Sentiment Analysis Used Already
 Derwent Capital Markets - ”The twitter
hedgefund”
 £25m fund
 10% of tweets
 predicts Dow Jones movement direction with
87.6% accuracy
 Returned 1.85% in its first month of trading
 Johan Bollen, Indiana University, used bag-of-
words approach

4. Sentiment Analysis Used Already
 Product reviews / ratings

5. Sentiment Analysis Used Already
 Social Media Analytics

6. Design Decision
 Many paragraphs of text (Product Reviews)
+ : Better accuracy of prediction
- : Less data overall
 Huge amount of small quantities of text (Twitter)
+ : Opinions of greater number of people
& at high enough frequency to model as a signal
- : Classification of opinion is v. poor
=> TWITTER

7. 2 Current Aims (will change later)
1. Project aims to be context
independent (i.e. Movies & products)
2. When context is given, use it to
better classify tweets

8. 1: Sentiment Analysis of Tweets
 Three-tier classification process:
tweet
spam not spam
objective subjective
positive negative

9. 1: Sentiment Analysis of Tweets
 Double-Back Propagation Algorithm
 ACL Journal, March 2011, MIT Press
 Opinion Word Extraction & Target Extraction
 4 rules
 ”The phone has a good screen”
=> add ”good” to list of adjectives
=> add ”screen” to list of nouns
 Etc.
 Great for rating features of a product
 Not great for tweets

10. 1: Sentiment Analysis of Tweets
 Twitter Part Of Speech (POS) tagger:
www.ark.cs.cmu.edu/TweetNLP/
 Written in java " ^
Drive ^
 Max Ent " ^
, ,
go V
and &
watch V
it O
! ,
Fantastic A
movie N
. ,

11. Bootstrapped Tweet SA improver
Tweet
IMDB Movie
Review Corpora Tweet
Tweet
Sentiment
Analysis
Tweet
Double-Back
Prop. Algo Tweet
Tweet
Gives useful adjectives, nouns Tweet

12. 2: Building a Network
 Collected my twitter friends, friends of friends,
friends of friends of friends.
 => 115,896 users

13. 2: Building a Network

14. 2: Building a Network
 Community detection:
 Paper 1: Near linear time algorithm for
detecting community structures on large
scale networks
 Paper 2: An LDA-based Community Structure
Discovery Approach for Large-Scale Social
Networks Haizheng Zhang

15. 2: Building a Network
 Like MapReduce
 Instead of ”map” and ”reduce”
 Map = 'Update':
modify overlapping sets of data
 Reduce = 'Sync': perform reductions in the
background while sync is running
 Label Propagation & LDA

16. 3: Time series prediction
 Will get time series from python to R
using the rpy2 module
 R has a great package ”quantmod” for
importing financial market data.
 Can also import other time series
very easily & many great libraries.

17. Built With
 Python - For majority of code
Packages: numpy, scipy, matplotlib
networkx, graphviz, rpy2
django, twython, nltk
 R - For time series analysis
 Postgreql - SQL database
 Java - Twitter POS tagger
 C/C++ - GraphLab

18. End Product
IMDB Movie
Review Corpora Tweet
Tweet
Sentiment
Tweet
Double-Back Analysis
Prop. Algo Tweet
Tweet

19. Thank You
 Mike Davies
 Documented at www.m1ked.com

20. Notes: Vowpal Wabbit LDA
Vowpal Wabbit is an open source library
for fast online learning (mostly SGD)
mainly developed by a guy at Yahoo.
 Optimised for speed
 LDA uses clever tricks like vectorisation,
floating point representation to avoid using
pow() and exp() functions.

21. Notes: Label Propagation
 Label Propagation has been proven to be an
effective semi-supervised learning approach in
many applications. The key idea behind label
propagation is to first construct a graph in which
each node represents a data point and each
edge is assigned a weight often computed as
the similarity between data points, then
propagate the class labels of labeled data to
neighbors in the constructed graph in order to
make predictions.