Scraped Twitter to obtain tweets from New York Times, CNN and Trump. Investigated effects of tweets on business confidence. Built prediction model using NLP and ML.

1. Predicting Business Confidence using News and Social Media
Why Predict Business Confidence?
Financial
Crisis
John Cai, University of Cambridge 1
Why use News Media and Social Media data?
Data Collection
8000+
CNN Articles containing
“US Economy” scraped
92000+
Tweets containing “US
Economy” obtained
32000+
Tweets obtained from
@realDonaldTrump
6000+
NYT Articles from the
Economy Section scraped
News Data Twitter Data
Fig 1: Business Confidence vs GDP Growth
1. Financial Crisis: The drop in business confidence preceded US GDP growth data by 1 month.
2. News: Scraped using Selenium, Beautiful Soup and Newspaper in Python.
3. Tweets: Obtained using Python’s Twitterscraper .
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Trump
Presidency
Lead Indicator
OECD Business Confidence
Index (BCI) is a key lead
indicator of GDP growth, as
shown in Fig 1. BCI measures
expectations using surveys.
Infrequency
OECD Business Confidence
Index is published monthly.
We do not currently have
daily or weekly estimates of
business confidence.
High Frequency
Sentiments in news media
and social media change in
real-time. We are able to
construct estimates of daily
business confidence.
Broad-based
Traditional estimates of daily
sentiments are from financial
markets. News and Social
Media capture sentiments in
the broader real economy.
- VIX index (uses option prices to measure volatility)
- S&P500 daily returns and change in daily returns
Financial Data
(1)
Note: Y-axis is the standardized value of the variables

2. Building a Prediction Model using NLP and ML
John Cai, University of Cambridge 2
1. NLTK: developed by Stanford and trained on movie reviews. VADER: Developed by Georgia Tech and trained on tweets.
2. Cross-validation using h-step ahead: Calculates MSPE by taking the actual value – forecasted value from the model.
3. Plot of Squared Prediction Error over time, to see how the relative performance of the two models varies over the test period.
Use Textblob to tokenize the
textual data at the words level
and the sentence level. Remove
stop-words and creating n-grams.
NLP
Perform Features Scaling with
scikit-learn by standardizing all
features. Set aside 20% of the
data for Testing (2016-2018) and
another 20% for Validation.
1. Data Pre-processing
Employ VADER’s lexical and rule-
based classifier and NLTK’s
Naïve Bayes Classifier to obtain
polarity and/or subjectivity.
2. Sentiment Analysis
Compute the average and cross-
sectional variance of sentiments
over every month. Omit features
with unit roots.
3. Feature Engineering
4. Data Preparation
Employ Cross-Validation LASSO
with a rolling forecast origin and
fixed window (adapted for time-
series). LASSO selects features
with high prediction value.
5. Training the Model
0
0.1
0.2
0.3
0.4
Oct-16 Apr-17 Oct-17 Apr-18 Oct-18
Less Parsimonious More Parsimonious
ML
Fig 2: Test-Set Squared Error
!"#$ , which corresponds to a
more parsimonious model, is
preferred as the model has a
smaller MSPE for out-of-sample
predictions (shown in Fig 2).
6. Model Selection
Training subset Validation
Get Mean Squared Prediction
Error (MSPE) over the Test Set
Cross Validation Loop
Tunes hyper-parameters by
minimizing the MSPE from h-
step ahead forecasting over
the Validation SetGives penalty terms !%&' and !"#$,
which allows us to select
features.
Training Set Test
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3. John Cai, University of Cambridge 3
1. LASSO selected the VADER Score rather than the NLTK Score, likely because VADER is trained on Social Media data.
2. Uncertainty is reflected in sentiment variance. In recessions, uncertainty and sentiment variance increase (Bloom, 2018).
3. Markov Switching Models would account for the structural breaks expected during recessions (Hamilton, 2010).
Fig 3: Best Prediction Model (!"#$) Fig 4: Model Evaluation
Neutral BullishBearish
High
Error
Very Low
Error
Low
Error
Limitations and ExtensionsResults and Implications
1. Twitter sentiments are
most informative of BCI
Twitter has the highest value in
prediction compared to NYT,
CNN and Trump’s Tweets.
2. Results are consistent
with economic theory
Variance of sentiments predicts
business confidence because it
is counter-cyclical (-- coefficent).
Features Selected (sign of
coeff)
1. Twitter Polarity Mean (+)
2. Twitter Polarity Variance (--)
3. VIX Index (--)
4. Returns (+)
5. Lagged Returns (+)
6. Lagged BCI (+)
3. Model works best during
neutral and bullish periods
As shown in Fig 4, the squared
prediction error is much higher
for bearish periods.
4. Possible extension:
Markov Switching Models
Performance in bearish periods
could improve if Markov chains
are used for structural breaks.
Analyzing and Evaluating Results from the Prediction Model
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