1. < sen·ti·ment >
the prevailing attitude of investors as to anticipated
price development in a market.
Tim Harbers, CTO SNTMNT
DataScienceNL Meetup November 8th 2012

2. Tim Harbers
Background
BSc Computer Science
MSc Computer Science
Researcher
Data Miner
Technical Consultant
Co-Founder and COO
Co-Founder and CTO

3. The Rockstars
Vincent van Leeuwen
Customer Development
‣ Balanced multidisciplinary team
‣ Two machine learning experts in
predictive analysis and large
Kees van Nunen datasets
Product Development
‣ Academic degrees in Behavioral
Finance, Portfolio Finance,
Strategic Management & Artificial
Durk Kingma
Intelligence
Data Mining Expert
‣ Strong network in (Dutch) financial
industry
‣ Young, enthusiastic team with a
Tim Harbers proven entrepreneurial mindset
Machine Learning Expert

4. How to select the right stock
to invest in?

5. Our solution:
Predicting stock price movement
based on online buzz
Engineered based on academic research:
Van Leeuwen (2011) Bollen, et al, (2010)
Sprenger and Welpe (2010) Sehgal and Song (2007)

6. Why would this work?
 Very different from traditional indicators
 News travels faster via social than traditional media
 Tremendous amount of data
 (Almost) nobody uses it yet

7. Why focus on Twitter?
 Public data & easily accessible
 Structured language
 400M tweets per day

8. Historic Research
Bollen (2010)
Created a model based on Twitter mood states, which
was 86% accurate on the DJI.
Sprenger and Welpe (2011)
Analyzed correlation of the stock market and micro
blogs

9. Financial Sentiment vs Brand Sentiment
Financial Sentiment Brand Sentiment
 Tweets relating to  Tweets relating to
stocks brands
 Written by traders  Written by consumers
 Trader mumbo jumbo  Any language
 More relevant  Larger dataset
 Shorter term  Longer term

10. Data setup
Period
June 2010 to April 2012
Stocks
Top 15 most tweeted stocks in S&P 500
Tweets
Financial Dataset Timm Sprenger (4 million)
4 Million tweets Topsy Brand Tweets (100+ million tweets)
Other
Klout
Peerindex

12. Sentiment
Scoring

13. Financial tweets

14. Commercial tweets

15. Sentiment analysis:
Enabling computers to derive sentiment
from natural language

16. Naive Approach: Dictionaries
 Use a dictionary of common positive and negative
terms
 Count the number of positive and negative terms
 Use the difference between the two.

17. SNTMNT’s approach: machine learning
 Label a training set of tweets (target)
 Use preprocessing techniques
 Use several feature extractors
 Create a sparse dataset.
 Use supervised learning to train a machine learning
model.

18. Labeling
• 25K Financial tweets hand labeled
• 30K Commercial tweets hand labeled
• 1M #happy vs. #sad

19. Difficulties in sentiment analysis
 Authors / Urls
 Foreign languages
 Slang
 aykm
 lol
 tgsttttptct
 Negation
 Target Sentiment Analysis

20. Results
Financial tweets
84.3% accurate on 2-point scale (Baseline: 60.4%)
76.8% accurate on 3-point scale (Baseline: 65.0%)
Beat Lexalytics (84.3% vs. 70.3%)
Commercial tweets
84.7% accurate on 2-point scale (Baseline: 61.0%)
86.9% accurate on 3-point scale (baseline: 81.1%)

21. Stock

22. Stock Regression
 Input:
 Sentiment scores
 Mood states
 Meta Data
 Stock
 Output:
 Trading Indication
 Confidence

23. Many dimensions
 Tweet period
 Trading period
 Financial Tweets or Commercial Tweets
 Tweet Crunchers
 Models
 Trading strategy

24. Tweet Aggregation Problem
 Tweet volume
 Volume positive tweets
 Avg sentiment
 Sentiment Growth
 Etc.

25. Machine Learning Models
 Linear Regression
 Bayesian Approaches
 Decision Trees
 Neural Nets
 Support Vector Machines

26. Results
 R2 < 0.01
 Not usable as an independent trading model after
transaction costs.
 Still usable as an extra indicator to be used by proven
trading models.

27. Products - next steps:
Sentiment APIs Stock Dashboard Trading Indicator API
(B2B) (B2B2C) (B2B)
‣ Market leader and
thought leader financial
sentiment analysis.
‣ Getting more insights
‣ Extend scope to further into added value of
niche domains and SNTMNT algorithm as
languages. indicator next to
fundamental and
technical analysis.

28. For more info, visit:
www.SNTMNT.com
Any questions?