Predicting the future with social media

What The Future Holds For Social
Media Data Analysis
Predictive analytics using Twitter data
Peter Wlodarczak wlodarczak@gmail.com

Agenda
 Introduction
 Research methodology
 Applications
 Challenges
 Conclusions

Introduction I
 Shift from publisher-generated to user-
created content
 90% of the content on the Internet is now
user generated (Graham et al. 2011)
 Unprecedented amount of opinionated
data on the Internet
 Online social networks (OSN) are one of
the biggest data sources of the internet
(Oboler, Welsh & Cruz 2012)

Introduction II
 Opinions can be expressed on the
Internet without programming knowledge
(Web 2.0)
 Opinions are key influences of human
behavior
 People increasingly consult the Internet
before making decisions

Introduction III
 OSN give new insights into peoples
opinions, interests and views
 Social networking Web sites are amassing vast
quantities of data
 Computational social science is providing tools to
process this data (Oboler, Welsh & Cruz 2012)
 Social computing, a new paradigm of computing
and technology development, has become a
central theme across a number of information and
communication technology fields (Wang et al.
2007, p. 79)

Introduction IV
 Growing interest in Social Media Mining
(SMM) in the market
 Gnip, Klout, DataSift and Sprout social specialized
in SM data analysis
 Apple bought Topsy for 200 million US dollars
(Harris 2013)
 TV stations buy Facebook data to see how
popular their shows are (Rusli 2013)
 No surveys necessary

Introduction V
 Research in the area of computational
social science and Big Data
 Social computing is a cross-disciplinary research
and application field with theoretical underpinnings
including both computational and social sciences
(Wang et al. 2007, p. 80)
 Big Data is the ability of society to harness
information in novel ways to produce useful
insights or goods and services of significant value
(Mayer-Schonberger & Cukier 2013, p. 2)

Introduction VI
 Analyzing data to:
 Understand the underlying structure of it
and gain knowledge
 Make predictions from new, unseen
examples

Introduction VII
 Current behavior indication for future
decisions
 New area of research: predictive
analytics
 Machine learning techniques used for
prediction
 Learning from experience, “data”, to predict
future behavior of individuals
 Support decision making process

Introduction VIII
 Big Data
 Big Data is usually defined by the three
V’s. Volume, velocity and variety (Klein,
Tran-Gia & Hartmann 2013, p. 320)
 High volume
 Created at high velocity
 Structured, semi-structured and unstructured

Introduction IX
 Big Data principles
 No sample selection, all data analysed
 Data doesn’t have to be of high quality
 Structured and unstructured data

Introduction X
 Data mining
 Techniques for finding and describing
structural patterns in data
 Tool for helping to explain that data and
make predictions from it (Witten, Frank &
Hall 2011, p. 8)
 Used to
 gain knowledge
 make predictions

Introduction XI
 Data analysis steps
 Analyze mood by means of sentiment
analysis
 Create time series and correlate it to real
world phenomenon
 Make predictions based on new data
 Support decision making process

Introduction XII
 Social Media data has been analysed to
predict
 Financial indicators (Bollen, Mao & Zeng
2010)
 Elections (Tumasjan et al. 2011)
 Box office revenue (Asur & Huberman 2010)
 Disease outbreak (Achrekar et al. 2011)
 Natural disasters (Sakaki, Okazaki and
Matsuo 2010)

Research methodology I
 Predictive analysis of Social Media
consists of two phases
 Data conditioning phase
 Predictive analysis phase

Research methodology II
 Determination of time window
 Selection of search terms
 Selection of data extraction method
Collection and
filtering of raw
data
 Selection of prediction variables
 Measurement of predictor variables
Computation
of Predictor
Variables
Data Conditioning
Phase
 Selection of predictive method
 Identification of data for evaluation of prediction
Creation of
Predictive
Mode
 Selection of the evaluation method
 Specification of the prediction baseline
Evaluation of the
Predictive
Performance
Predictive Analysis
Phase
Analysis phases

Research methodology III
Input and output variables
Twitter
sentiments
Share price
Future
share price
Expressed as binary
sentiment
classification
Expressed in
dollars
Expressed in
dollars

Research methodology IV
Mood towards
Apple
Number of
Tweets
Apple stock
price

Data collection and analysis overview
Data collection
•Query Twitter
through API
•Store in
MongoDB
Preprocessing
•Remove
stopwords
•Remove
Tweets with
Links
Model
evaluation
•Classification
algorithm
•Neural
network
Time series
•Twitter volume
•Binary
sentiment
classification
Correlation
• Correlation
between
sentiment and
financial data
 Collection and analysis steps overview
 Some steps like model evaluation are
iterative

Data collection I
Data collection
•Query Twitter
through API
•Store in DB
Preprocessing
•Remove
stopwords
•Remove
Tweets with
Links
Model
evaluation
•Classification
algorithm
•Neural
network
Time series
•Twitter
volume
•Binary
sentiment
classification
Correlation
• Correlation
between
sentiment and
financial data
DB

Data collection II
Data Source
Twitter
Query API
Firehose API
Gardenhose API
Data Store
MongoDB
 Historic data collected through Twitter
APIs
 Timestamp, message text, region

Data collection III
 Data collected through Twitter query
API
 Using the Java programming language
 Using the Twitter4j library
 Stored as JSON (JavaScript Object
Notation) in a MongoDB

Data collection IV
public void runQuery() {
Twitter twitter = new TwitterFactory().getInstance();
AccessToken accessToken = new AccessToken(ACCESS_TOKEN, ACCESS_TOKEN_SECRET);
twitter.setOAuthConsumer(CUSTOMER_KEY, CUSTOMER_SECRET);
twitter.setOAuthAccessToken(accessToken);
try {
Query query = new Query(“$Appl");
QueryResult result;
result = twitter.search(query);
List<Status> tweets = result.getTweets();
for (Status tweet : tweets) {
System.out.println("@" + tweet.getUser().getScreenName() + " - " + tweet.getText());
}
}
catch (TwitterException te) {
te.printStackTrace();
System.out.println("Failed to search tweets: " + te.getMessage());
System.exit(-1);
}
}
Twitter query algorithm to retrieve Tweets on Apple

Data preprocessing I
Data collection
•Query Twitter
through API
•Store in DB
Preprocessing
•Remove
stopwords
•Remove
Tweets with
Links
Model
evaluation
•Classification
algorithm
•Neural
network
Time series
•Twitter
volume
•Binary
sentiment
classification
Correlation
• Correlation
between
sentiment and
financial data

Data preprocessing II
 Remove stop-words, “the”, “then”, “at” …
 Punctuation, apostrophe, brackets, colon ..
 Discard Tweets with no explicit statements
like “Going to the Apple store”
 Discard irrelevant Tweeds like “I love apples
and pears”
 Discard possible spam by discarding Tweets
that match the regular expression “http:” and
“www”

Data preprocessing III
 Machine learning algorithms don’t take text
as input
 Create feature vector
 Word frequencies
 n-grams, unigram, bigram, trigram …
 “good”, “very good”, “not very good”
 Create sentiment lexicon
 Sentiment analysis highly domain specific
 “This mattress had a valley after one month”
 “This car uses a lot of fuel”

Model evaluation I
Data collection
•Query Twitter
through API
•Store in DB
Preprocessing
•Remove
stopwords
•Remove
Tweets with
Links
Model
evaluation
•Classification
algorithm
•Neural
network
Time series
•Twitter
volume
•Binary
sentiment
classification
Correlation
• Correlation
between
sentiment and
financial data
90.2 %
84.7 %
97.3 %
Neural Network
Naïve Bayes
Nearest Neighbor

Model evaluation II
 Experience shows that no single machine
learning scheme is appropriate to all data
mining problems (Witten, Frank & Hall 2011,
p. 403)
 Different algorithms are trained
 The best performing algorithm will be
selected

Model evaluation III
 Data classification and analysis through
 Machine learning techniques
 System can learn from data, e. g. detect spam
 Finding and describing structural patterns in
data and generalize
 Data classification is a supervised
learning problem
 Class label is known

Model evaluation IV
 Other machine learning models are
 Unsupervised learning
 Class label is unknown
 Used for cluster analysis
 Semi-supervised learning
 Small amount of labeled data, big volumes of
unlabeled data

Model evaluation V
 Model evaluation through iterative supervised
machine learning process
 Select classification algorithm, Naïve Bayes, k-
NN, Decision tree induction …
 Find a function ƒ that classifies Tweets into
positive and negative Tweets
 Data is divided into training and test data
 Model is trained using the training data
 Trained model is verified using the test data

Model evaluation VI
 Determine through loss function how well the
model performs on future, unseen data
 Calculate error:
 Training error = fraction of training examples misclassified
 Test error = fraction of test examples misclassified
 Generalization error = probability of misclassifying new
random example

Model evaluation VII
 Testing determines the classification
accuracy
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑜𝑟𝑟𝑒𝑐𝑡 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝑠
𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑒𝑠𝑡 𝑐𝑎𝑠𝑒𝑠
 Simple but very optimistic since training data
is used for testing

Model evaluation VIII
 n-fold cross-validation
 Divide data into n folds, where typically 4 < n < 11
 Data divided randomly into n folds
 n – 1 folds used for training, 1 holdout fold for
testing
 Error rate is calculated on the holdout fold
 repeated n times such that each fold is the holdout
fold once
 Error estimate is averaged over all n error rates

Model evaluation IX
 Typical data mining task goes through many
iterations
 As many iterations as necessary till result is
satisfying, i. e. accuracy converges
 Best data mining scheme is selected
 Used against unseen data for classification
 Can be used on real-time data

Model evaluation X
RapidMiner workbench

Model evaluation XI
Training data
sex mask cape tie ears smokes class
Batman male yes yes no yes no Good
Robin male yes yes no no no Good
Alfred male no no yes no no Good
Penguin male no no yes no yes Bad
Catwoman female yes no no yes no Bad
Joker male no no no no no Bad
Test data
Batgirl female yes yes no yes no ?
Riddler male yes no no no no ?

Model evaluation XII
 Description of data:
 Generalisation for new examples
if sex = male and mask = yes and cape = yes
and tie = yes and ears = yes and smokes = no
then character = Good
if mask = yes and ears = yes and smokes = no
then character = Good

Model evaluation XIII
tie
no yes
cape smokes
no yes no yes
bad badgood good

Model evaluation XIV
 Trees must be:
 Big enough to fit training data
 Big enough to capture true patterns
 Not too big (Ockham’s razor):
 Overfitting
 Capture noise
 Find spurious patterns

Model evaluation XIV
 Best tree size cannot be determined
from training error
Schapire 2004

Model evaluation XV
Schapire 2004

Model evaluation XVI
 For building an accurate classifier:
 Enough training examples
 Good performance on training set
 Classifier that is not too complex
 Strategy for controlling tree size:
 Build large tree that fully fits training data
 Prune back

Model evaluation XVII
 Grow on just part of the training data, then
prune using minimum error on held out
data

Classifiers I
 Decision trees:
 Best known:
 C4.5 (Quinlan), successor C5.0
 CART for classification and regression trees
(Breitman et al.)
 Fast to train and evaluate
 Relatively easy to interpret
 Accuracy often not satisfactory

Classifiers II
 Perceptron (Neuron)
 Linear classifier
 Data linearly separable using a hyperplane
 Where w = weights, a = real-valued vector,
feature vector, a0 = bias
 Binary classifier f(a) that maps its input
vector a to a single, binary output value
w0a0 + w1a1 + w2a2 + … + wkak = 0

Classifiers III
w0
1
bias
attr
a1
attr
a2
attr
a3
w1 w2
w3
f(a) = kwkak + b
f(a) > 0 or
f(a) < 0

Classifiers IV
 Multilayer Perceptron
 Non-linear classifier
 Perceptrons are connected in a
hierarchical structure

Classifiers V
 Not all data is linearly separable

Classifiers VI
1
bias
attr
a1
attr
a2
Input layer Hidden layer Output layer

Classifiers VII
 Multilayer Perceptron
 Perceptrons organized in several layers
 All layer is fully interconnected with the next
layer
 All nodes except input node are perceptrons
 Feedforward neural network
 Uses backpropagation for training
 Error propagated back to minimize loss function

Classifiers VIII
 Allows to get approximate solutions for
very complex problems
 Support Vector Machines (SVM) are a
much simpler alternative to ANN
 Many more classifiers
 k-Nearest Neighbor
 Naïve Bayes
 …

Data classification I
Data collection
•Query Twitter
through API
•Store in DB
Preprocessing
•Remove
stopwords
•Remove
Tweets with
Links
Model
evaluation
•Classification
algorithm
•Neural
network
Time series
•Twitter
volume
•Binary
sentiment
classification
Correlation
• Correlation
between
sentiment and
financial data

Data Classification II
 Data classification:
 Binary mood polarity: positive, negative
 Represented graphically as time series
Positive Tweets
Negative Tweets

Correlations I
Data collection
•Query Twitter
through API
•Store in DB
Preprocessing
•Remove
stopwords
•Remove
Tweets with
Links
Model
evaluation
•Classification
algorithm
•Neural
network
Time series
•Twitter
volume
•Binary
sentiment
classification
Correlation
• Correlation
between
sentiment and
financial data
Sentiment polarity
Share price

Correlations II
 Finding correlations:
 Binary sentiment classification time series
compared against stock price over same
time frame
 Does the number of positive Tweets
preceding a soar of Apple stock price?

Correlations III
Microsoft stock price (Yahoo! Finance 2014)

Correlations IV
Tweet polarity and MSFT stock price

Correlations V
 If there are correlations in historic data,
trained model used against real time
data
 Access real time Tweets using Twitters
streaming API
 Firehose API (100% of real time Tweets)
 Gardenhose API (10% of real time Tweets)
 Spritzer API (1% of real time Tweets)

Correlations VI
 Since correlations are most certainly non
linear, correlating has to be automated
 Bivariate Granger causality test
 Determine whether one time series can be
used to predict another
 If X in a time series causes Y = Granger-
cause
 X provides statistical significant information
about Y

Correlations VII
 Granger test examines linear causality
among bivariate or multivariate time series
 Many real world phenomenon are not
linear
 Non-linear extensions to Granger have
been developed
 Other correlation techniques
 Phase Slope Index measures temporal flux
between time series

Correlations VII
 More robust than Granger since more
immune against noise
 Machine learning techniques such as
ANN can be used for finding
correlations

Applications I
 Technologies for predictive analysis
have matured
 IBM SPSS
 Stata
 SAS

Applications II
 Free open source
 WEKA
 Partly open source
 RapidMiner
 Cloud solutions
 IBM WatsonAnalytics
 Google BigQuery
 SAS Cloud Analytics

Challenges I
 Real word data often very poor quality
 Social Media vast, noisy and
unstructured
 Getting relevant posts is challenging
 Spam has become a serious issue
 Detecting sarcasm very difficult
 Political opinions full of irony and sarcasm
 Data preprocessing one of the most
important steps

Challenges II
 Opinion mining remains challenging
task
 Overall statement often difficult to
determine
 No ground truth
 Not everybody is using Social Media
 Self-selection bias

Conclusions I
 Predictive analysis poses many
interesting research problems
 Many opportunities for future research
 Determining the credibility of posts (catfish,
sock puppet)
 Better filtering mechanisms
 More research in Machine Learning
than feature extraction

Conclusions II
 Correlation does not mean causation
 Finding causative mechanism for
correlation

Thank you for the attention
 Questions?

References I
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