This document summarizes a study analyzing Twitter sentiment about the release of Windows 10 from July 28, 2015 to August 5, 2015. The study used machine learning techniques including logistic regression, decision trees, and random forests to predict sentiment polarity from tweets. Key factors found to influence polarity included the number of days since release, tweet length, and whether the Twitter user was a seasoned or new user. The study found sentiment shifted from positive to negative later in the sample period. It recommends Microsoft continue positive branding post-release and target influential, seasoned Twitter users.

1. INST 737 – Twitter
Sentiment Prediction on
#Windows10 release
Anuj Sharma, Krishnesh Pujari and Rajesh
Gnanasekaran
12/03/15

2. Objective
• Twitter in the recent time has come at par to other social
Medias such as Facebook, Google+ and Myspace in terms
of creating sentiment waves on any issue around the
world.
• To perform a twitter sentiment analysis and sentiment
prediction on Microsoft’s Windows 10 release which took
place on July 29th of this year.
• Follow semi-supervised learning technique to create
target variable and use it in the classification models.
• To analyze and interpret the results and provide
recommendations to Microsoft.

3. About the Data
• Imported using NodeXL from Twitter Search Network
• Original dataset had 9000+ observations on hashtag
‘#Windows10’ for the time period between July 28th 2015
till August 05th 2015
• After cleaning (missing, duplicate, other language) ended up
with 4646 observations with 28 original factors, 19 derived
features
• Performed feature engineering to arrive at these additional
features as we felt they might be better used to predict the
target factor, i.e, “Polarity”
• Types of Variables - Categorical, Continuous

4. Sentiment Analysis
● Tweet text cleaning - remove filler words, ignore words
which are not in english
● Used a customized R code for text mining which parsed
tweets and classified the words into +ve, -ve or neutral
polarities
● The code compared the words in the tweets with a
dictionary and mapped the polarity with the tweet.
● Cross checked for the correct functionality of the code by
creating 100 odd tweets and manually checked the
polarity

5. Exploring the Data
● Created histograms and box plots to identify any unusual
behavior between the variables. Found some interesting
patterns

6. Continued...
● Tested the variables
over Pearson’s
Correlation; found
significant correlation
between factors like
Tweets and Followed.
Made sure that we did
not include both these
variables together in
logistic regression.
● Momentum of tweets
shifted from +ve-
neutral to -ve at the
end period of sample;
almost 80% of -ve
tweets on 08/05

7. Feature Engineering
● Tweet timestamp was broken into Tweet date and Tweet
time
● Current Date
● Days difference = Tweet date minus upgrade date
● Number of weeks since joined Twitter
● Number of months since joined Twitter
● Log of number of months since joined Twitter
● Log of number of followers
● Log of number of people followed by the user
● Log of number of favorites
● Log of number of tweets
● Length of Tweet

8. Multinomial Logistic Regression
and Interpretation
● Multinomial over Binomial - Target variable has more
than two values.
● To check which factors are affecting the tweet polarity in
any manner.
● Interpret using Log of odds to see the variation.
● Variables of importance: Relationship, No. of followers,
Tweet length, No. of weeks since joined twitter

9. Results

10. Decision Trees Classification
and Interpretation
● Decision trees are the alternative to logistic regression
● CART (Classification and Regression Trees) method is
used to recursively classify the target variable
● Variables of importance: Tweet date, Days difference and
length of the tweet

11. Results

12. Random Forest Classification
and Interpretation
● Random Forest is an ensemble of decision trees which
will helps in better prediction of polarity
● Implemented 501 decision trees to identify important
predictors of polarity
● Variables of importance: Tweet date, Days difference and
length of tweet

13. Results

14. Limitations
● The dataset was for a short span of time between 07/28/15 and
08/05/15, if bigger dataset sample, results may differ
● We have limited the scope of this project to tweets only in English
language.
● We were not able to take advantage of the geo-spatial coordinates
as most of the records had n/a value.
Recommendations
● As the negative sentiment starts to prevail post release in
the later half of the week, Microsoft should not stop on
the positive branding even post release.
● As the tweet coming from a seasoned twitter user is
more likely to be negative, Microsoft should target those
influential accounts to spread positive word.

15. Thank You !