Slide deck for text mining project

1. Gauging Market Reaction Based
on Twitter Sentiment

2. About
I would like to see if it is possible to use text mining methods to
predict sentiment about companies expressed on Twitter,
furthermore I would like to see if what people are saying (positive
or negative) about a company is reflected in its stock price.

3. Part One: Data

4. 3 Sources of Data
1. Kaggle (For training sentiment models)
2. Twitter API (Acquiring tweets for sentiment analysis)
3. IEX Developer Platform API & Yahoo Finance (Financial
Information)

5. 1. Kaggle Data
• 1.4 million tweets rated either 0 (neg) or 4 (pos)
• 50/50 Training split of 0’s (535984) and 4’s (536016)
• Used for training sentiment classification models
• Actually sentiment140 data (or very similar)

6. 2. Twitter Data
• API request returns JSON file with 100 tweets for given date time
and search term. Limited to 250 requests with sandbox premium.
• Search term for API was “[Ticker Name] OR [Company Name]”
• Request 30 days for company we want to analyze, total of 3,000
(30 * 100) tweets
• Mean sentiment was predicted for each dates 100 tweet batch
• CSV exported containing 30 dates and 30 mean sentiment scores

7. 3. Stock Market Data
• Stock market data was used later in my analysis to test multiple
hypotheses.
• For the analysis stock data and tweet sentiment were joined by
their date
• Used two primary financial indicators:
1. Daily Return
2. Close Price

8. Part Two: Model

9. Multinomial Naïve Bayes: Preprocessing
• 2 Step Preprocessing:
1. Regex Cleanse (tons of emoji)
2. Vectorization
• Unigram
• Tfidf weighting
• Doc freq = 5
• Stop words = ‘English’

10. Multinomial Naïve Bayes: Evaluation
• Model accuracy with holdout test
(1/3 split): 77.29%
• Model accuracy with 3-fold cross
validation: 77.38%

11. Multinomial Naïve Bayes: Evaluation

12. Multinomial Naïve Bayes: Real Performance

13. Part Three: Analysis

14. Monthly Sentiment Report
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
11/5/1811/6/1811/7/1811/8/1811/9/18
11/10/18
11/11/18
11/12/18
11/13/18
11/14/18
11/15/18
11/16/18
11/17/18
11/18/18
11/19/18
11/20/18
11/21/18
11/22/18
11/23/18
11/24/18
11/25/18
11/26/18
11/27/18
11/28/18
11/29/18
11/30/1812/1/1812/2/1812/3/18
Sentiment Time-Series for Month
ABX MSFT TSLA

15. TSLA: Tesla
Daily Return Regression Close Price Regression
R² = 0.0371
-0.08
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08
0 0.5 1 1.5 2 2.5 3 3.5 4
Tesla– Daily Return & Sentiment
R² = 0.0152
320
325
330
335
340
345
350
355
360
365
0 0.5 1 1.5 2 2.5 3 3.5 4
Tesla– Close Price & Sentiment

16. ABX: Barrick Gold
Daily Return Regression Close Price Regression
R² = 0.0003
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
Barrick Gold– Daily Return & Sentiment
R² = 0.0279
12.2
12.4
12.6
12.8
13
13.2
13.4
13.6
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
Barrick Gold– Close Price & Sentiment

17. MSFT: Microsoft
Daily Return Regression Close Price Regression
R² = 0.172
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0.05
0 0.5 1 1.5 2 2.5 3 3.5 4
Microsoft– Daily Return & Sentiment
R² = 0.1099
100
102
104
106
108
110
112
114
0 0.5 1 1.5 2 2.5 3 3.5 4
Microsoft– Close Price & Sentiment

18. Questions?

19. Sources
• http://help.sentiment140.com/for-students