1. Vincent Fiore, Ange Assoumou, Debarshi Dutta, Kenneth Almodovar
The Correlation between the Topic and Emotion of Tweets
through Machine Learning
Vincent Fiore, Ange Assoumou, Debarshi Dutta, Kenneth Almodovar

2. Our Project Classifying Tweets based on topic and emotion. Happy Sad
Angry
Religion
Politics
Family

3. Purpose Searching for a correlation between a topic and an emotion.
Using machine learning to classify Tweets, then plotting these data points against each other.

4. Background Many papers have been written on similar topics
We used these to get an idea of where to start our research. Performed a literature review in each of these topics to develop a better background.
Ideas on preprocessing Tweets.
Using word lists.
Edges cases to keep in mind and avoid.

5. Methodology Manually categorizing Tweets. Creation of large word lists
Used to train the ML algorithm
Creation of large word lists
Used to create the feature set to train the algorithm with
This is the raw data that is fed into the algorithm
Turns Tweets into number
Confirming the training has worked
Classifying Tweets beyond the original data set
Plotting these new classifications to search for correlations

6. Manual Classification and Word Lists
Incredibly difficult process due to the nature of the topics.
Emotion is easy, but topics can be vague. Politics, for example, changed wildly since this data set was created in 2009.
Word lists are also challenging
Need to balance amount of words on list with possible occurrences. In one case, one emotion’s list was much larger than the others, so almost every Tweet was preliminarily marked as that emotion. This had to be fixed.

7. Implementation
Using the manual classification, we created 6 different lists in the following categories:
Politics
Religion
Family
Anger
Happy
Sadness
Each of these contains hundreds of words.

8. Implementation (continued)
Figure 1.2
The figure shows an example of
The Word Lists
Our biggest list contained more than 600 words.

9. Implementation (continued)
Figure 1.3 Display the rate of words per tweet

10. Results
Each classifier, on their own, scored roughly 90% accuracy on the test data set
This meant that, when compared to our pre-classified tweets, the algorithm was roughly three times more accurate than random guessing. (33%, IE: randomly choosing one of the three categories)

11. Results Classification of Donald Trump’s tweets
Classification of Pope Francis’s tweets

12. Results Clear trends for individual users For the most part:
President was political and either sad or angry
Religious leaders were religious and happy
The Dalai Lama
The Dalai Lama, for example, was overwhelmingly positive

13. Results Need for further study
Classifier worked best with users whose tweets fell into a clear category.
Left: Nancy Pelosi’s tweets as classified. They are usually political, but show a large number of religious tweets.

14. Results Religious tweets are over represented
For tweets that are not classified as political, family, or religious, the classifier tended to categorize these as religious. This means that certain users’ tweets are overly classified as religious. Due to the nature of machine learning, tracking down this issue proved too problematic.

15. Results
Tweets from Dr. Phil, left, illustrate the issue of overclassification of religion. However, the emotion behind the tweet is still correct.

16. Conclusion
It proved difficult to extrapolate the results for a universal data set
Most users tend to tweet in their own specific style. For the most part, this doesn’t transcend all users and topics. There are certain topics that do show a correlation, like religion and sadness, but for the most part, emotion and topic do not show an obvious link.
The link only exists for users and their own style of tweeting. IE: Politicians speak about politics and religious leaders about religion. Emotion is even more specific in its relation to each individual.

17. Further study
Needs to focus on gathering much larger samples of tweets.
This will help weed out any possible trends between the two subjects in Twitter at large.
Also needs further development time of the classifier
On its own, each classifier was scoring roughly 90% accuracy, but this dropped once combined. The error level was multiplied, causing errors like the religion issue that was encountered.